[r-cran-zelig] 34/102: Import Upstream version 2.7~1

Andreas Tille tille at debian.org
Sun Jan 8 16:58:14 UTC 2017


This is an automated email from the git hooks/post-receive script.

tille pushed a commit to branch master
in repository r-cran-zelig.

commit cb6fb503ce9a9baa6fc1f99b86e0eb283e479f1d
Author: Andreas Tille <tille at debian.org>
Date:   Sun Jan 8 09:39:09 2017 +0100

    Import Upstream version 2.7~1
---
 DESCRIPTION                                 |     8 +-
 NAMESPACE                                   |    14 +-
 R/Diff.R                                    |     5 +
 R/S4Utilities.R                             |    54 +
 R/arima.wrap.R                              |     3 +
 R/eiRxC.R                                   |   213 +
 R/lag.eps.R                                 |     3 +
 R/lag.y.R                                   |     3 +
 R/misc.vglm.R                               |    36 -
 R/parse.formula.R                           |    37 +-
 R/plot.zelig.arima.R                        |   172 +
 R/print.arimaSummary.R                      |    11 +
 R/qi.vglm.R                                 |     2 +-
 R/setx.Arima.R                              |    54 +
 R/setx.default.R                            |     2 +-
 R/sim.setxArima.R                           |   218 +
 R/summary.Arima.R                           |     4 +
 R/summary.zelig.arima.R                     |    57 +
 R/zelig.default.R                           |     8 +-
 R/zelig2arima.R                             |    70 +
 data/approval.tab                           |    66 +
 demo/00Index                                |     1 +
 demo/arima.R                                |    75 +
 demo/bivariate.probit.R                     |     4 +-
 inst/doc/.latex2html-init                   |   244 +
 inst/doc/Makefile                           |    16 +
 inst/doc/Zcommands.tex                      |   767 +
 inst/doc/acknowledgments.aux                |    33 +
 inst/doc/acknowledgments.tex                |    36 +
 inst/doc/addingModels.tex                   |   496 +
 inst/doc/advancedR.tex                      |   560 +
 inst/doc/announce.txt                       |    68 +
 inst/doc/commands/describe.mymodel.tex      |   206 +
 inst/doc/commands/matchit.tex               |    29 +
 inst/doc/commands/matchitref.tex            |   390 +
 inst/doc/commands/mi.tex                    |    36 +
 inst/doc/commands/model.end.aux             |    41 +
 inst/doc/commands/model.end.tex             |    49 +
 inst/doc/commands/model.frame.multiple.aux  |    42 +
 inst/doc/commands/model.frame.multiple.tex  |    80 +
 inst/doc/commands/model.matrix.multiple.aux |    42 +
 inst/doc/commands/model.matrix.multiple.tex |   111 +
 inst/doc/commands/names.tex                 |    57 +
 inst/doc/commands/parse.formula.aux         |    44 +
 inst/doc/commands/parse.formula.tex         |   113 +
 inst/doc/commands/parse.par.aux             |    42 +
 inst/doc/commands/parse.par.tex             |    95 +
 inst/doc/commands/plot.ci.aux               |    43 +
 inst/doc/commands/plot.ci.tex               |    86 +
 inst/doc/commands/plot.zelig.aux            |    42 +
 inst/doc/commands/plot.zelig.tex            |    68 +
 inst/doc/commands/print.aux                 |    41 +
 inst/doc/commands/print.tex                 |    51 +
 inst/doc/commands/put.start.aux             |    41 +
 inst/doc/commands/put.start.tex             |    44 +
 inst/doc/commands/replicate.aux             |    42 +
 inst/doc/commands/replicate.tex             |    82 +
 inst/doc/commands/rocplot.aux               |    43 +
 inst/doc/commands/rocplot.tex               |    91 +
 inst/doc/commands/set.start.aux             |    42 +
 inst/doc/commands/set.start.tex             |    53 +
 inst/doc/commands/setx.aux                  |    46 +
 inst/doc/commands/setx.tex                  |   149 +
 inst/doc/commands/sim.aux                   |    41 +
 inst/doc/commands/sim.tex                   |   152 +
 inst/doc/commands/summary.aux               |    43 +
 inst/doc/commands/summary.tex               |   125 +
 inst/doc/commands/tag.aux                   |    42 +
 inst/doc/commands/tag.tex                   |    54 +
 inst/doc/commands/ternaryplot.aux           |    42 +
 inst/doc/commands/ternaryplot.tex           |    81 +
 inst/doc/commands/ternarypoints.aux         |    42 +
 inst/doc/commands/ternarypoints.tex         |    63 +
 inst/doc/commands/zelig.tex                 |   185 +
 inst/doc/faq.tex                            |   293 +
 inst/doc/figs/increase.eps                  | 12290 +++++++++++
 inst/doc/figs/increase.pdf                  |   Bin 0 -> 30671 bytes
 inst/doc/figs/roc.eps                       |   396 +
 inst/doc/figs/roc.pdf                       |   401 +
 inst/doc/figs/sample1.eps                   |   914 +
 inst/doc/figs/sample1.pdf                   |   568 +
 inst/doc/figs/sample2.eps                   |   695 +
 inst/doc/figs/sample2.pdf                   |   709 +
 inst/doc/figs/ternary.eps                   |  2182 ++
 inst/doc/figs/ternary.pdf                   |  2220 ++
 inst/doc/figs/vertci.eps                    |   962 +
 inst/doc/figs/vertci.pdf                    |   820 +
 inst/doc/graphs.tex                         |   296 +
 inst/doc/index.shtml                        |    95 +
 inst/doc/inference/gee.tex                  |    76 +
 inst/doc/install.R                          |    12 +
 inst/doc/install.tex                        |   426 +
 inst/doc/intro.tex                          |   137 +
 inst/doc/models/2sls.tex                    |   205 +
 inst/doc/models/3sls.tex                    |   181 +
 inst/doc/models/arima.aux                   |    43 +
 inst/doc/models/arima.tex                   |   320 +
 inst/doc/models/blogit.aux                  |    47 +
 inst/doc/models/blogit.tex                  |   326 +
 inst/doc/models/bprobit.aux                 |    47 +
 inst/doc/models/bprobit.tex                 |   357 +
 inst/doc/models/coda_diag.tex               |    32 +
 inst/doc/models/contributors.tex            |    15 +
 inst/doc/models/contributors2.tex           |    11 +
 inst/doc/models/contributors_systemfit.tex  |     4 +
 inst/doc/models/ei.dynamic.aux              |    47 +
 inst/doc/models/ei.dynamic.tex              |   283 +
 inst/doc/models/ei.hier.aux                 |    46 +
 inst/doc/models/ei.hier.tex                 |   287 +
 inst/doc/models/eiRxC.aux                   |    42 +
 inst/doc/models/eiRxC.tex                   |   212 +
 inst/doc/models/exp.aux                     |    46 +
 inst/doc/models/exp.tex                     |   275 +
 inst/doc/models/factor.bayes.aux            |    46 +
 inst/doc/models/factor.bayes.tex            |   260 +
 inst/doc/models/factor.mix.aux              |    46 +
 inst/doc/models/factor.mix.tex              |   272 +
 inst/doc/models/factor.ord.aux              |    46 +
 inst/doc/models/factor.ord.tex              |   244 +
 inst/doc/models/gamma.aux                   |    50 +
 inst/doc/models/gamma.tex                   |   235 +
 inst/doc/models/irt1d.aux                   |    46 +
 inst/doc/models/irt1d.tex                   |   250 +
 inst/doc/models/irtkd.aux                   |    46 +
 inst/doc/models/irtkd.tex                   |   252 +
 inst/doc/models/logit.aux                   |    52 +
 inst/doc/models/logit.bayes.aux             |    45 +
 inst/doc/models/logit.bayes.tex             |   238 +
 inst/doc/models/logit.tex                   |   270 +
 inst/doc/models/lognormal.aux               |    46 +
 inst/doc/models/lognormal.tex               |   262 +
 inst/doc/models/ls.aux                      |    51 +
 inst/doc/models/ls.tex                      |   241 +
 inst/doc/models/mlogit.aux                  |    45 +
 inst/doc/models/mlogit.bayes.aux            |    45 +
 inst/doc/models/mlogit.bayes.tex            |   274 +
 inst/doc/models/mlogit.tex                  |   249 +
 inst/doc/models/mloglm.tex                  |   105 +
 inst/doc/models/negbin.aux                  |    50 +
 inst/doc/models/negbin.tex                  |   237 +
 inst/doc/models/normal.aux                  |    50 +
 inst/doc/models/normal.bayes.aux            |    45 +
 inst/doc/models/normal.bayes.tex            |   230 +
 inst/doc/models/normal.tex                  |   273 +
 inst/doc/models/ologit.aux                  |    45 +
 inst/doc/models/ologit.tex                  |   244 +
 inst/doc/models/oprobit.aux                 |    44 +
 inst/doc/models/oprobit.bayes.aux           |    45 +
 inst/doc/models/oprobit.bayes.tex           |   273 +
 inst/doc/models/oprobit.tex                 |   252 +
 inst/doc/models/poisson.aux                 |    50 +
 inst/doc/models/poisson.bayes.aux           |    45 +
 inst/doc/models/poisson.bayes.tex           |   230 +
 inst/doc/models/poisson.tex                 |   219 +
 inst/doc/models/probit.aux                  |    50 +
 inst/doc/models/probit.bayes.aux            |    45 +
 inst/doc/models/probit.bayes.tex            |   253 +
 inst/doc/models/probit.tex                  |   220 +
 inst/doc/models/relogit.aux                 |    52 +
 inst/doc/models/relogit.tex                 |   398 +
 inst/doc/models/sur.tex                     |   188 +
 inst/doc/models/systemfit_output.tex        |    55 +
 inst/doc/models/tobit.aux                   |    46 +
 inst/doc/models/tobit.bayes.aux             |    47 +
 inst/doc/models/tobit.bayes.tex             |   262 +
 inst/doc/models/tobit.tex                   |   208 +
 inst/doc/models/weibull.aux                 |    46 +
 inst/doc/models/weibull.tex                 |   267 +
 inst/doc/newModels.tex                      |   615 +
 inst/doc/refman.tex                         |   186 +
 inst/doc/syntax.tex                         |   588 +
 inst/doc/upquote.sty                        |    76 +
 inst/doc/zelig.aux                          |   364 +
 inst/doc/zelig.bbl                          |   206 +
 inst/doc/zelig.blg                          |   103 +
 inst/doc/zelig.jpg                          |   Bin 0 -> 14314 bytes
 inst/doc/zelig.log                          |  4189 ++++
 inst/doc/zelig.out                          |   130 +
 inst/doc/zelig.pdf                          | 28203 ++++++++++++++++++++++++++
 inst/doc/zelig.tex                          |   372 +
 inst/doc/zelig.toc                          |   544 +
 inst/zelig.pdf                              | 28039 -------------------------
 man/approval.Rd                             |    27 +
 183 files changed, 76580 insertions(+), 28116 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 2742f0f..68e8df2 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,12 +1,12 @@
 Package: Zelig
-Version: 2.6-5
-Date: 2006-09-14
+Version: 2.7-1
+Date: 2006-11-04
 Title: Everyone's Statistical Software
 Author: Kosuke Imai <kimai at Princeton.Edu>,
         Gary King <king at harvard.edu>,
         Olivia Lau <olau at fas.harvard.edu>
 Maintainer: Olivia Lau <olau at fas.harvard.edu>
-Depends: R (>= 2.3.0), MASS, boot
+Depends: R (>= 2.4.0), MASS, boot
 Description: Zelig is an easy-to-use program that can estimate, and
         help interpret the results of, an enormous range of
         statistical models. It literally is ``everyone's statistical
@@ -22,4 +22,4 @@ Description: Zelig is an easy-to-use program that can estimate, and
         translates them into quantities of direct interest.
 License: GPL version 2 or newer
 URL: http://gking.harvard.edu/zelig
-Packaged: Thu Sep 14 12:37:37 2006; king
+Packaged: Sat Nov  4 21:44:22 2006; king
diff --git a/NAMESPACE b/NAMESPACE
index 70d4d5a..51603fc 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -40,10 +40,10 @@ export( current.packages,
 	zeligDescribeModelXML
 )
 	
-S3method("$", vglm)
-S3method("$", summary.vglm)
-S3method("$<-", vglm)
-S3method("$<-", summary.vglm)
+S3method("$", ZeligS4vglm)
+S3method("$", summary.ZeligS4vglm)
+S3method("$<-", ZeligS4vglm)
+S3method("$<-", summary.ZeligS4vglm)
 S3method(formula, vglm)
 S3method(model.matrix, multiple)
 S3method(model.frame, multiple)
@@ -53,6 +53,7 @@ S3method(names, vglm)
 S3method(names, zelig)
 S3method(names, zelig.relogit)
 S3method(plot, zelig)
+S3method(plot, zelig.arima)
 S3method(plot.zelig, "2sls")
 S3method(plot.zelig, "3sls")
 S3method(plot.zelig, beta)
@@ -74,6 +75,7 @@ S3method(plot.zelig, relogit)
 S3method(plot.zelig, sur)
 S3method(plot.zelig, w2sls)
 S3method(plot.zelig, weibull)
+S3method(print, arimaSummary)
 S3method(print, BetaReg)
 S3method(print, names.relogit)
 S3method(print, names.zelig)
@@ -92,11 +94,13 @@ S3method(print, eiRxC)
 S3method(print, zelig)
 S3method(repl, default)
 S3method(repl, zelig)
+S3method(sim, setxArima)
 S3method(sim, default)
 S3method(sim, cond)   
 S3method(sim, counter)
 S3method(sim, setx.MI)
 S3method(sim, setx.strata)
+S3method(setx, Arima)
 S3method(setx, default)
 S3method(setx, eiRxC)
 S3method(setx, noX)
@@ -105,6 +109,8 @@ S3method(setx, strata)
 S3method(setx, relogit2)
 S3method(summarize, array)
 S3method(summarize, ei)
+S3method(summary, Arima)
+S3method(summary, zelig.arima)
 S3method(summary, BetaReg)
 S3method(summary, MI)
 S3method(summary, strata)   
diff --git a/R/Diff.R b/R/Diff.R
new file mode 100644
index 0000000..9d4b58d
--- /dev/null
+++ b/R/Diff.R
@@ -0,0 +1,5 @@
+Diff <- function(Y, d, ds=NULL, per=NULL){
+  mc <- match.call()
+  name <- as.name(mc[[2]])
+  list(name=name, d=d, ds=ds, per=per)
+}
diff --git a/R/S4Utilities.R b/R/S4Utilities.R
new file mode 100644
index 0000000..029aff8
--- /dev/null
+++ b/R/S4Utilities.R
@@ -0,0 +1,54 @@
+###
+## Creates a Zelig class of type S4 which inherit
+## from the class of "object".
+## If class of the object is "XXX" then the new class
+## will be called "ZeligS4XXX"
+## A new object is created from the new class and
+## all the slots of the object
+## input   : an object of class S4
+## output  : a new object of class s4 with 2 additional
+##           slots
+
+
+create.ZeligS4 <- function(object){
+
+  if(isS4(object)) {
+    s4ClassName<- class(object)[[1]]
+    newClassName <- paste("ZeligS4",s4ClassName,sep="")
+    tmp <- paste("setClass(\"",newClassName,"\",representation(\"",s4ClassName,"\",zelig.data=\"data.frame\",zelig=\"character\"),where=.GlobalEnv)",sep="")
+    
+    ##create the new class
+    eval(parse(text=tmp))
+    
+    ## initialize a new object of that class
+    tmp<- paste("new(\"",newClassName,"\")",sep="")
+
+    res<-eval(parse(text=tmp))
+    
+    ## copy over all the slots from teh existing object
+    ## to the new object
+    for(s in slotNames(object)){
+      slot(res,s)<-slot(object,s)
+    }
+    return (res)
+  } else {
+    return(object)
+    ##  error("\"object\" should be of class S4")
+  }
+}
+
+
+"$.ZeligS4vglm" <- "$.summary.ZeligS4vglm" <- function(a, b){
+  if(is.na(pmatch(b, slotNames(a))))
+    stop(paste("no such slot exists in", a))
+  else
+    slot(a, b)
+}
+
+"$<-.ZeligS4vglm" <- "$<-.summary.ZeligS4vglm" <- function(a, b, value){
+  if(is.na(pmatch(b, slotNames(a))))
+    stop(paste("no such slot exists in", a))
+  else
+    slot(a, b)<-value
+  return(a)
+}
diff --git a/R/arima.wrap.R b/R/arima.wrap.R
new file mode 100644
index 0000000..9099291
--- /dev/null
+++ b/R/arima.wrap.R
@@ -0,0 +1,3 @@
+arima.wrap <- function(formula, order, x, xreg=NULL, data, M, ...){
+  arima(x=x, xreg=xreg, order=order, ...)
+} 
diff --git a/R/eiRxC.R b/R/eiRxC.R
new file mode 100644
index 0000000..c13bdfb
--- /dev/null
+++ b/R/eiRxC.R
@@ -0,0 +1,213 @@
+callparamseiestim <- function(formula,data, covar = NULL,
+                              const = 0.001, parSeed = NULL, ...){
+  
+
+  tmp <- cmei(formula=formula, data=data, covar=covar)
+  data <- tmp$data
+  nR<-tmp$nR
+  nC <- tmp$nC
+
+ if(!(is.null(covar))){
+  covar<-tmp$covar
+}
+
+  mf <- match.call()
+  t <- terms(mf$formula)
+  out <- paramsei.estim(data=data, covar=covar, nR=nR, nC=nC, const=const, 
+                        parSeed=parSeed)
+  out$terms <- t
+
+  return (out)
+}
+
+# CALL.DIFP
+# Calculates penalty for given parameters
+# p     - parameter vector R x (C-1)
+# mx    - Column marginals
+# my    - row marginals
+# nR    - number of rows
+# nC    - number of columns
+# nP    - number of precincts
+# const - weight for penalty
+# covar
+
+call.difp <- function(par, mx, my, covar, nR, nC, nP, const){
+  g <- par[1:(nR*(nC-1))]
+  if(is.numeric(covar)) {  
+    d <- par[(nR*(nC-1)+1):(2*nR*(nC-1))]
+    gamma <- array(0, dim = c(nR, nC-1, nP))
+    diff <- 0
+    for (i in 1:nP) {
+      gamma[,,i] <- matrix(g + covar[i]*d, nrow = nR, ncol = nC-1, byrow = TRUE)
+      expo <- exp(gamma[,,i]) 
+      if (nC != 2) 
+        ebeta <- exp(gamma[,,i]) / (1 + apply(exp(gamma[,,i]), 1, sum))
+      else 
+        ebeta <- exp(gamma[,,i]) / (1 + exp(gamma[,,i]))
+      yhat <- mx[i,] %*% ebeta
+      diff <- diff + sum((yhat - my[i, -nC])^2) + (const*sum(gamma[,,i]^2))
+    }
+  }
+  else {
+    d <- array(0, dim = nR * (nC-1))
+    gamma <- matrix(g, nrow = nR, ncol = nC-1, byrow = TRUE)
+    ebeta <- exp(gamma)/(1 + apply(exp(gamma), 1, sum))
+    diff <- sum((mx %*% ebeta - my[, -nC])^2) + (const * sum(gamma^2))
+  }
+  diff
+}
+
+
+# Penalized Least Square Minimizer
+# PARAMS.ESTIM
+# Estimates parameters minimizing the penalized least squares criterion
+# x       - index (optional, for bootstrapping)
+# data    - marginals (optionally with covariates)
+# nR      - number of rows
+# nC      - number of columns
+# const   - weight for penalty
+# parSeed - Seed for parameters (optional)
+
+paramsei.estim <- function(data, covar=NULL, nR, nC, const=0.001, 
+                           parSeed=NULL) {
+  
+  normalizedata <- function(dt){
+    s <- apply(dt,1,sum)
+    return (dt/s) 
+  }
+
+  mc <- match.call(expand.dots = TRUE)
+  #print(colnames(data))
+
+  socialnames <- colnames(data[,1:nR])
+  mx <- as.matrix(data[,1:nR])
+  mx <- normalizedata(mx)
+  for (i in 1:nrow(mx)){
+    if (round(sum(mx[i,]), digits=4) != 1){
+      stop ("The Sum of each row marginal (\"social classes\") should be 1. It seems like you have a problem in the row nr. ", i)
+    }
+  }
+  partiesnames <- colnames(data[,(nR+1):(nR+nC)])
+  my <- as.matrix(data[,(nR+1):(nR+nC)])
+  my <- normalizedata(my)
+  for(i in 1:nrow(my)){
+    if (round(sum(my[i,]),digits=4)!= 1){
+      stop ("The Sum of each column marginal (\"parties\") should be 1. It seems like you have a problem in the row nr. ", i)
+    }
+  }
+  nP <- nrow(data)
+
+  coef.names<-c()
+  for(i in 1:(nR)){
+    for(j in 1:(nC-1)){
+      coef.names <- c(coef.names, paste(socialnames[[i]], ".",
+                                        partiesnames[[j]], sep = ""))
+    }
+  }
+  if(!is.null(covar)){
+    if(is.null(parSeed)) parSeed = rnorm(2*nR*(nC-1))
+    for (i in 1:(nR*(nC-1)))
+      coef.names<-c(coef.names,paste("delta",i,sep=""))
+    names(parSeed)<-coef.names
+  }
+  else {
+    if(is.null(parSeed))  parSeed = rnorm(nR*(nC-1))
+    names(parSeed)<-coef.names
+  }
+
+  fit <- optim(parSeed, fn = call.difp, method="L-BFGS-B", hessian=TRUE,
+               covar = covar, nR = nR, nC = nC, nP = nP, mx = mx,
+               my = my, const = const)
+  fit$coefficients <- fit$par
+  fit$cov1<-diag(1/diag(fit$hessian))
+  fit$vcov <- solve(fit$hessian)
+  fit$hessian<-fit$hessian
+  fit$terms <- attr(all.vars, "terms")
+  fit$call <- mc
+  fit$contrasts <- attr(all.vars, "contrasts")
+  fit$xlevels <- attr(all.vars, "xlev")
+  fit$levels <- attr(all.vars, "lev")
+  fit$dims <- c(nR, nC, nP)
+  fit$covar <- covar
+  fit$socialnames <- socialnames
+  fit$partiesnames <- partiesnames
+  class(fit) <- "eiRxC"
+  return(fit)
+}
+
+
+# Calculate Fractions
+# CALC.FRACTIONS
+# Calculate fractions from the parameters
+# p     - parameters
+# nR    - number of rows
+# nC    - number of columns
+# covar - (Optional) Vector of covariates
+
+calc.fractions <- function(object, simpar) {
+  nR <- object$dims[1]
+  nC <- object$dims[2]
+  d <- array(0, dim = nR*(nC-1))
+  if (!is.null(object$covar)) {
+    covar<-as.matrix(object$covar)
+    nP <- nrow(covar)
+    ests <- array(0, dim = c(nR, nC, nP),
+                  dimnames=list(object$socialnames,
+                    object$partiesnames,
+                    c(1:nP)))
+    d <-object$coefficients[(nR*(nC-1)+1):(2*nR*(nC-1))]
+    for(i in 1:nP) {
+      estsTmp<- array(0, dim = c(nR, nC, nrow(simpar)))
+      for(j in 1:nrow(simpar)){
+        g <- simpar[j,1:(nR*(nC-1))]
+        p.exp <- exp(g + d*covar[i])
+        p.matrix <- matrix(p.exp, nrow = nR, byrow = TRUE)
+   
+        p.sums <- apply(p.matrix, 1, sum)
+        p.sums <- p.sums + 1
+        p.less <- p.matrix/p.sums
+        estsTmp[,,j] <- cbind(p.less, 1 - apply(p.less, 1, sum))
+      }
+      ests[,,i]<-apply(estsTmp,c(1,2),mean)
+    } 
+  }
+  else {
+    ests <- array(0, dim = c(nR, nC, nrow(simpar)),
+                  dimnames=list(object$socialnames,
+                    object$partiesnames,
+                    c(1:nrow(simpar))))
+    for(i in 1:nrow(simpar)){
+      g<-simpar[i,1:(nR*(nC-1))]
+      p.matrix <- matrix(exp(g), nrow = nR, byrow = TRUE)
+      p.sums <- apply(p.matrix, 1, sum)
+      p.sums <- p.sums + 1
+      p.less <- p.matrix / p.sums
+      ests[,,i] <- cbind(p.less, 1 - apply(p.less, 1, sum))
+    }
+  }
+  return (ests)
+}
+
+
+cmei <- function(formula, data, covar = NULL, ...){
+  if (is.null(rownames(data))) {
+    rownames(data) <- 1:nrow(data)
+    assign(data, as.character(mc$data), env = .GlobalEnv)
+  }
+  res<-NULL
+  myVars<-all.vars(formula[[2]])
+  mxVars<-all.vars(formula[[3]])
+  allVars<-c(mxVars,myVars)
+  nR<-length(mxVars)
+  nC <-length(myVars)
+  if(!(is.null(covar))){
+    covar <- model.frame(covar, data)
+    res$covar<-as.matrix(covar)
+  }
+  else
+    res$covar<-NULL
+  res$data<-data[,allVars]
+  res$nR<-nR
+  res$nC<-nC
+  return(res)
+}
diff --git a/R/lag.eps.R b/R/lag.eps.R
new file mode 100644
index 0000000..d8f5cc7
--- /dev/null
+++ b/R/lag.eps.R
@@ -0,0 +1,3 @@
+lag.eps<- function(q, qs=NULL){
+  list(q=q, q.s=qs, ep=TRUE, y=FALSE)
+}
diff --git a/R/lag.y.R b/R/lag.y.R
new file mode 100644
index 0000000..63bf316
--- /dev/null
+++ b/R/lag.y.R
@@ -0,0 +1,3 @@
+lag.y <- function(p, ps=NULL){
+  list(p=p, p.s = ps, ep=FALSE, y=TRUE)
+}
diff --git a/R/misc.vglm.R b/R/misc.vglm.R
deleted file mode 100644
index c89a701..0000000
--- a/R/misc.vglm.R
+++ /dev/null
@@ -1,36 +0,0 @@
-"$.vglm"<-"$.summary.vglm"<-function(a, b){
-  if(is.na(pmatch(b, slotNames(a)))){
-    pmt<-pmatch(b, names.default(a))
-    if(is.na(pmt))
-      stop(paste("no such object exists in", a))
-    else
-      a[[pmt]]
-  }
-  else
-    slot(a, b)
-}
-
-"$<-.vglm"<-"$<-.summary.vglm"<-function(a, b, value){
-  if(is.na(pmatch(b, slotNames(a)))){
-    tmp<-class(a)
-    class(a)<-NULL
-    if(length(a)==0){
-      a[[1]]<-value
-      names(a)[1]<-b
-    }
-    else{
-      pmt<-pmatch(b, names(a))
-      if(is.na(pmt)){
-        n<-length(names(a))
-        a[[n+1]]<-value
-        names(a)[n+1]<-b
-      }
-      else
-        a[[pmt]]<-value
-    }
-    class(a)<-tmp
-  }
-  else
-    slot(a, b)<-value
-  a
-}
diff --git a/R/parse.formula.R b/R/parse.formula.R
index f2b6c97..debe9e7 100644
--- a/R/parse.formula.R
+++ b/R/parse.formula.R
@@ -24,31 +24,20 @@ parse.formula<-function( formula, model,data=NULL){
 
   
   checkNrOpt<-function(modelNumEqn,nrUserOpt,modelParsOpt,userOpt){
-    if(length(modelNumEqn)==1){
-      if(nrUserOpt > modelNumEqn)
-        stop("The parameter \"",modelParsOpt,"\" requires ",modelNumEqn, " equation(s) with DepVar=FALSE and ExpVar=TRUE. You have provided ", nrUserOpt, " See model doc. for more details")
-      else{
-        if(nrUserOpt<modelNumEqn)
-          for(i in (nrUserOpt+1):modelNumEqn){
-            userOpt[[i]]<-as.formula("~1")
-            if(modelNumEqn==1)
-              names(userOpt)[[i]]<-modelParsOpt
-            else
-              names(userOpt)[[i]]<-paste(modelParsOpt,i,sep="")
-          }
-      }
-    }else{
-      if(!(betweenf(nrUserOpt,modelNumEqn)))
-        if(nrUserOpt < modelNumEqn[[1]])
-          for(i in (nrUserOpt+1):modelNumEqn[[1]]){
-            userOpt[[i]]<-as.formula("~1")
-            names(userOpt)[[i]]<-paste(modelParsOpt,i,sep="")
-          }
-        else
-          stop("The parameter \"",modelParsOpt,"\" requires between ",modelNumEqn[[1]]," and ",modelNumEqn[[2]], " equation(s). You have provided ", nrUserOpt, " See model doc. for more details")    
-    }
+            if(!(betweenf(nrUserOpt,modelNumEqn)))
+              if(nrUserOpt < modelNumEqn[[1]]){
+                      if(modelNumEqn[[1]]==1)
+                        userOpt[[modelParsOpt]]<- as.formula("~1")
+                      else
+                        for(i in (nrUserOpt+1):modelNumEqn[[1]]){
+                                userOpt[[i]]<-as.formula("~1")
+                                names(userOpt)[[i]]<-paste(modelParsOpt,i,sep="")
+                        }
+              }else
+            stop("The parameter \"",modelParsOpt,"\" requires between ",modelNumEqn[[1]]," and ",modelNumEqn[[2]], " equation(s). You have provided ", nrUserOpt, " See model doc. for more details")    
+    
     return(userOpt)
-  }
+}
   
   betweenf<-function(a,range){
     if (is.finite(range[[2]]))
diff --git a/R/plot.zelig.arima.R b/R/plot.zelig.arima.R
new file mode 100644
index 0000000..cc2e293
--- /dev/null
+++ b/R/plot.zelig.arima.R
@@ -0,0 +1,172 @@
+
+plot.zelig.arima <- function(x, xlab="", user.par=FALSE, pred.se=TRUE,
+                             col=c("blue", "red", "green3", "black"),
+                             lty=2, ...){
+  if (length(col) > 4) col <- rep(col, 4)[1:4]
+  k <- length(x$qi)
+  if (k==3){
+    if (is.null(x$qi$t.eff) & !user.par){
+      par(mfrow=c(1,1))
+    }	
+    if (!is.null(x$qi$t.eff) & !user.par){
+      par(mfrow=c(2,1))
+    }
+    if (user.par){
+      par <- par(no.readonly = TRUE) 
+    }
+    ev.mean <- apply(x$qi$ev, 2, "mean")
+    ev.quant <- apply(x$qi$ev, 2, "quantile", c(0.025, 0.975))
+    if (pred.se){
+      ev.total.up <- x$qi$ev + 1.96*x$qi$se
+      ev.total.down <- x$qi$ev - 1.96*x$qi$se
+      ev.total.up <- apply(ev.total.up, 2, "mean")
+      ev.total.down <- apply(ev.total.down, 2, "mean")
+      min.plot <- min(ev.quant[1,], ev.total.down, x$t.series[1:x$min.time])
+      max.plot <- max(ev.quant[2,], ev.total.up, x$t.series[1:x$min.time])
+    }
+    if (!pred.se){
+      min.plot <- min(ev.quant[1,], x$t.series[1:x$min.time])
+      max.plot <- max(ev.quant[2,], x$t.series[1:x$min.time])
+    }
+    if (length(ev.mean)>1){
+      ts.plot(x$t.series[1:x$min.time],
+              ylim=c(min.plot, max.plot),
+              col=col[4],ylab="Time Series Value",
+              xlim=c(1, x$min.time + ncol(x$qi$ev)))
+      lines(ev.mean~c((x$min.time+1):
+                      (x$min.time + ncol(x$qi$ev))),
+            col=col[1])
+      lines(ev.quant[2,]~c((x$min.time+1):
+                           (x$min.time + ncol(x$qi$ev))),
+            lty=lty, col=col[1])
+      lines(ev.quant[1,]~c((x$min.time+1):
+                           (x$min.time + ncol(x$qi$ev))),
+            lty=lty, col=col[1])
+      if (pred.se){
+        lines(ev.total.up~c((x$min.time+1):
+                            (x$min.time + ncol(x$qi$ev))),
+              lty=lty, col= col[3])
+        lines(ev.total.down~c((x$min.time+1):
+                              (x$min.time + ncol(x$qi$ev))),
+              lty=lty, col=col[3])
+      }
+      lines(c(x$min.time, x$min.time + 1),
+            c(x$t.series[x$min.time], ev.mean[1]), col=col[1])
+      abline(v=x$min.time, lty=1, col=col[2])
+      if(!is.null(x$qi$t.eff)){
+        teff.mean<- apply(x$qi$t.eff, 2, "mean")
+        teff.quant<- apply(x$qi$t.eff, 2, "quantile", c(0.025, 0.975))
+        ts.plot(teff.mean, xlab="Time After Counterfactual",
+                ylab="Difference", main="Y - E[Y|X]",
+                ylim=c(min(teff.quant[1,]), max(teff.quant[2,])), col=col[1])
+        lines(teff.quant[2,], lty=lty, col=col[1])
+        lines(teff.quant[1,], lty=lty, col=col[1])
+      }
+    }
+    if (length(ev.mean)==1){
+      ts.plot(x$t.series[1:x$min.time],
+              ylim=c(min.plot, max.plot),
+              col=col[4],ylab="Time Series Value",
+              xlim=c(1, x$min.time + ncol(x$qi$ev)), gpars=...)
+      points(x$min.time + 1, ev.mean)
+      if (pred.se){
+        arrows(x$min.time + 1 , ev.total.down,
+               x$min.time + 1, ev.total.up, col=col[2],
+               code=3, length=0.1, angle=90)
+      }
+      arrows(x$min.time + 1, ev.quant[1,], x$min.time + 1,
+             ev.quant[2,], col=col[1], code=3, length=0.1, angle=90)
+      lines(c(x$min.time, x$min.time + 1),
+            c(x$t.series[x$min.time], ev.mean[1]), col=col[1])
+      if(!is.null(x$qi$t.eff)){
+        teff.mean<- apply(x$qi$t.eff, 2, "mean")
+        teff.quant<- apply(x$qi$t.eff, 2, "quantile", c(0.025, 0.975))
+        plot(density(x$qi$t.eff), ylab="Density",
+             xlab="Difference", main="Y-E[Y|X]", ...)
+      }
+    }
+  }
+  if (k==4){
+    par(mfrow=c(2,1))
+    ev.mean<- apply(x$qi$ev, 2, "mean")
+    ev.quant<- apply(x$qi$ev, 2, "quantile", c(0.025, 0.975))
+    if(pred.se){
+      ev.total.up<- x$qi$ev + 1.96*x$qi$se
+      ev.total.down<- x$qi$ev - 1.96*x$qi$se
+      ev.total.up<- apply(ev.total.up, 2, "mean")
+      ev.total.down<- apply(ev.total.down, 2, "mean")
+      min.plot<- min(ev.quant[1,], x$t.series[1:x$min.time], ev.total.down)
+      max.plot<- max(ev.quant[2,], x$t.series[1:x$min.time], ev.total.up)
+    }
+    if(!pred.se){
+      min.plot<- min(ev.quant[1,], x$t.series[1:x$min.time])
+      max.plot<- max(ev.quant[2,], x$t.series[1:x$min.time])
+    }
+    if(length(ev.mean)>1){
+      ts.plot(x$t.series[1:x$min.time],
+              ylim=c(min.plot, max.plot),
+              col=col[4], ylab="Time Series Value",
+              xlim=c(1, x$min.time + ncol(x$qi$ev)), gpars=...)
+      lines(ev.mean~c((x$min.time+1):
+                      (x$min.time + ncol(x$qi$ev))), col=col[1])
+      lines(ev.quant[2,]~c((x$min.time+1):
+                           (x$min.time + ncol(x$qi$ev))),
+            lty=lty, col=col[1])
+      lines(ev.quant[1,]~c((x$min.time+1):
+                           (x$min.time + ncol(x$qi$ev))),
+            lty=lty, col=col[1])
+      if(pred.se){
+        lines(ev.total.up~c((x$min.time+1):
+                            (x$min.time + ncol(x$qi$ev))),
+              lty=lty, col= col[3])
+        lines(ev.total.down~c((x$min.time+1):
+                              (x$min.time + ncol(x$qi$ev))),
+              lty=lty, col=col[3])
+      }
+      lines(c(x$min.time, x$min.time + 1),
+            c(x$t.series[x$min.time], ev.mean[1]))
+      abline(v=x$min.time, lty=1, col=col[2])
+    }
+    if(length(ev.mean)==1){
+      ts.plot(x$t.series[1:x$min.time], ylim=c(min.plot, max.plot),
+              ylab="Time Series Value", col=col[4],
+              xlim=c(1, x$min.time + ncol(x$qi$ev)), gpars=...)
+      points(x$min.time + 1, ev.mean)
+      if(pred.se){
+        arrows(x$min.time + 1, ev.total.down, x$min.time + 1,
+               ev.total.up, col=col[2], code=3, length=0.1, angle=90)
+      }
+      arrows(x$min.time + 1, ev.quant[1,], x$min.time + 1, ev.quant[2,],
+             col=col[1], code=3, length=0.1, angle=90)
+      lines(c(x$min.time, x$min.time + 1),
+            c(x$t.series[x$min.time], ev.mean[1]), col=col[1])
+    }
+    fd.mean<- apply(x$qi$fd, 2, "mean")
+    fd.quant<- apply(x$qi$fd, 2, "quantile", c(0.025, 0.975))
+    if(length(fd.mean)>1){
+      ts.plot(fd.mean, col=col[1], ylim=c(min(fd.quant[1,]), max(fd.quant[2,])),
+              main="E[Y|X1] - E[Y|X]", xlab="Time From First Counterfactual",
+              ylab="Difference", gpars=...)
+      lines(fd.quant[2,], lty=lty, col=col[1])
+      lines(fd.quant[1,], lty=lty, col=col[1])
+    }
+    if(length(fd.mean)==1){
+      par(mfrow=c(2,1))
+      ts.plot(x$t.series[1:x$min.time], ylim=c(min.plot, max.plot),
+              ylab="Time Series Value", col=col[4],
+              xlim=c(1, x$min.time + ncol(x$qi$ev)), gpars=...)
+      points(x$min.time + 1, ev.mean)
+      if(pred.se){
+        arrows(x$min.time + 1, ev.total.down, x$min.time + 1, ev.total.up,
+               col=col[2], code=3, length=0.1, angle=90)
+      }
+      arrows(x$min.time + 1, ev.quant[1,], x$min.time + 1, ev.quant[2,],
+             col=col[1], code=3, length=0.1, angle=90)
+      lines(c(x$min.time, x$min.time + 1),
+            c(x$t.series[x$min.time], ev.mean[1]), col=col[1])
+      plot(density(x$qi$fd), main="E[Y|X1] - E[Y|X]",
+           xlab="Difference in Value", ylab="Density", ...)
+    }
+  }
+}
+
diff --git a/R/print.arimaSummary.R b/R/print.arimaSummary.R
new file mode 100644
index 0000000..e805f72
--- /dev/null
+++ b/R/print.arimaSummary.R
@@ -0,0 +1,11 @@
+print.arimaSummary <- function(x, digits = min(getOption("digits"), 3), ...) {
+  cat("\nModel:", x$zelig.call$model, "\n", sep = " ")
+  cat("Number of simulations: ", x$number.sim, "\n\n")
+  dimnames(x$test.array)[[3]] <- dimnames(x$test.array)[[3]]
+  cat("Available Quantities of Interest: \n")
+  for (i in 1:length(dimnames(x$test.array)[[3]])) {
+    cat("\n", dimnames(x$test.array)[[3]][i], "\n")
+    print(x$test.array[,,i], digits = digits, ...)
+  }
+  return(invisible())
+}
diff --git a/R/qi.vglm.R b/R/qi.vglm.R
index d857bdd..d2eace4 100644
--- a/R/qi.vglm.R
+++ b/R/qi.vglm.R
@@ -1,4 +1,4 @@
-qi.vglm <- function (object, simpar, x, x1=NULL, y = NULL) {
+qi.ZeligS4vglm <- function (object, simpar, x, x1=NULL, y = NULL) {
   model <- object$zelig
   cm <- object at constraints
   if (model=="mlogit")
diff --git a/R/setx.Arima.R b/R/setx.Arima.R
new file mode 100644
index 0000000..be0cfcf
--- /dev/null
+++ b/R/setx.Arima.R
@@ -0,0 +1,54 @@
+setx.Arima <- function(object, cond=FALSE, data=NULL, counter=NULL,
+                      pred.ahead=0, ...){
+  if (!is.null(counter)) 
+    warning("counter ignored in ARIMA models.")
+  t.effect <-cond 
+  mc <- match.call()
+  env <- parent.frame()
+  if (is.null(data)){
+    data <- eval(object$call$data, envir=env)
+  }
+  if(!is.null(data)){
+   data <- as.data.frame(data)
+ }
+  trew <- na.omit(pmatch(names(mc), colnames(data), duplicates.ok=TRUE))
+  wert <- na.omit(pmatch(colnames(data), names(mc), duplicates.ok=TRUE))
+  wert <- sort(wert)
+  if (length(trew>0)){
+    if (pred.ahead!=0){
+      warning("'pred.ahead' available only for prediction models\nwith no external regressors\npred.ahead being set to zero\n")
+      pred.ahead <- 0 
+    }
+    test.time <- unlist(lapply(1:length(wert),
+                               function(i, mc) eval(mc[[wert[i]]]$time), mc=mc))
+    max.time <- max(test.time)
+    min.time <- min(test.time)
+    if (max.time > length(object$residuals)){
+      data.new <- matrix(as.matrix(data[nrow(data),]), ncol=ncol(data),
+                         nrow=(max.time - length(object$residuals)), byrow=TRUE)
+      colnames(data.new) <- colnames(data)
+      dta <- as.matrix(rbind(data, data.new))
+      rownames(dta) <- 1:nrow(dta)
+    } 
+    else { dta  <- data[1:(max.time),] }
+    for (i in 1:length(trew)){
+      dta[eval(mc[[wert[i]]]$time, envir=env), trew[i]] <-
+        eval(mc[[wert[i]]]$c.fact, envir=env)
+    }
+    dta <- as.matrix(dta[,na.omit(pmatch(names(object$coef), colnames(data)))])
+  }
+  if (length(trew)==0) {
+    data.new <- matrix(as.matrix(data[nrow(data),]), ncol=ncol(data),
+                       nrow=pred.ahead, byrow=TRUE)
+    colnames(data.new) <- colnames(data)
+    dta <- as.matrix(rbind(data, data.new))
+    dta <- as.matrix(dta[, na.omit(pmatch(names(object$coef), colnames(data)))])
+    rownames(dta) <- 1:nrow(dta)
+    min.time <- length(object$residuals)
+    max.time <- length(object$residuals)
+  }
+  out <- list(dta=dta, min.time=min.time, max.time=max.time,
+              pred.ahead=pred.ahead, t.effect = cond)
+  class(out) <- "setxArima"
+  out
+}
diff --git a/R/setx.default.R b/R/setx.default.R
index e6998ac..ae64ac7 100644
--- a/R/setx.default.R
+++ b/R/setx.default.R
@@ -59,7 +59,7 @@ setx.default <- function(object, fn = list(numeric = mean, ordered =
     env <- parent.frame()
   ## original data
   if (is.null(data))
-    if (is.data.frame(object$data))
+    if (nrow(as.data.frame(object$zelig.data)) > 0)
       dta <- object$data
     else
       dta <- eval(object$call$data, envir = env)
diff --git a/R/sim.setxArima.R b/R/sim.setxArima.R
new file mode 100644
index 0000000..6ccbdb7
--- /dev/null
+++ b/R/sim.setxArima.R
@@ -0,0 +1,218 @@
+sim.setxArima <- function(object, x, x1 = NULL, num = 1000, prev = NULL,
+                     bootstrap = FALSE, bootfn = NULL, cond.data = NULL,
+                     max.iter=10, ...) {
+  require(mvtnorm)
+  if (bootstrap | !is.null(bootfn) | !is.null(cond.data)){
+    warning("boostrap, bootfn, and cond.data are ignored in ARIMA models")
+  }
+  t.effect <- x$t.effect
+  ##extracting out the series
+  if (is.data.frame(object$zelig.data))
+    dat <- object$zelig.data
+  else
+    dat <- eval(object$call$data, parent.frame())
+  series <- eval(eval(object$call$formula[[2]])$name, envir=dat)
+  pred.ahead <- x$pred.ahead
+  if (!is.null(prev)){
+    draw.parm <- prev
+  }
+  if (is.null(prev)){
+    draw.parm <- rmvnorm(num, mean=object$coef, sigma=object$var.coef)
+    ##we need to clean out the MA's and AR's that are non-stationary  
+    if (object$arma[1] >= 1){
+      for (i in 1:nrow(draw.parm)){
+        sr <- polyroot(c(1, -draw.parm[i, (1:object$arma[1])]))
+        if (any(Mod(sr)<1)){
+          draw.parm[i,object$arma[1] ] <- NA
+        }
+      }
+    }  
+    if (object$arma[2] >= 1){
+      for (i in 1:nrow(draw.parm)){
+        sr <- polyroot(c(1, draw.parm[i, ((object$arma[1] + 1):
+                                          (object$arma[1] + object$arma[2]))]))
+        if (any(Mod(sr)<1)){
+          draw.parm[i, (object$arma[2])] <- NA
+        } 
+      } 
+    }  
+    if (object$arma[3] >= 1){
+      for (i in 1:nrow(draw.parm)){
+        sr <- polyroot(c(1, -draw.parm[i, ((object$arma[1] + object$arma[2] + 1):
+                                           (object$arma[1] + object$arma[2] +
+                                            object$arma[3]))]))
+        if (any(Mod(sr)<1)){
+          draw.parm[i,(object$arma[3]) ] <- NA
+        }
+      }
+    } 
+    if (object$arma[4]>=1){
+      for (i in 1:nrow(draw.parm)){
+        sr <- polyroot(c(1, draw.parm[i,
+                                      ((object$arma[1] + object$arma[2] +
+                                        object$arma[3] + 1):
+                                       (object$arma[1] + object$arma[2] +
+                                        object$arma[3] + object$arma[4]))]))
+        if (any(Mod(sr)<1)){
+          draw.parm[i,(object$arma[4]) ] <- NA
+        }
+      }
+    } 
+    draw.parm <- na.omit(draw.parm)
+    iter <-0
+    while (nrow(draw.parm) < num){
+      iter <- iter + 1
+      if (iter <= max.iter){
+        draw.new <- rmvnorm(num, mean=object$coef, sigma=object$var.coef)
+        ##we need to clean out the MA's and AR's that are non-stationary  
+        if (object$arma[1] >= 1){
+          for (i in 1:nrow(draw.new)){
+            sr <- polyroot(c(1, -draw.new[i, (1:object$arma[1])]))
+            if (any(Mod(sr)<1)){
+              draw.new[i,object$arma[1] ] <- NA
+            }
+          }
+        }  
+        if (object$arma[2] >= 1){
+          for (i in 1:nrow(draw.new)){
+            sr <- polyroot(c(1, draw.new[i, ((object$arma[1] + 1):
+                                             (object$arma[1] + object$arma[2]))]))
+            if (any(Mod(sr)<1)){
+              draw.new[i, (object$arma[2])] <- NA
+            }
+          }
+        } 
+        if (object$arma[3] >= 1){
+          for (i in 1:nrow(draw.new)){
+            sr <- polyroot(c(1, -draw.new[i, ((object$arma[1] + object$arma[2] + 1):
+                                              (object$arma[1] + object$arma[2] +
+                                               object$arma[3]))]))
+            if (any(Mod(sr) < 1)){
+              draw.new[i,(object$arma[3]) ] <- NA
+            }
+          }
+        } 
+        if (object$arma[4] >= 1){
+          for (i in 1:nrow(draw.new)){
+            sr <- polyroot(c(1, draw.new[i, ((object$arma[1] + object$arma[2] +
+                                              object$arma[3] + 1):
+                                             (object$arma[1] + object$arma[2] +
+                                              object$arma[3] + object$arma[4]))]))
+            if(any(Mod(sr) < 1)){
+              draw.new[i,(object$arma[4]) ] <- NA
+            }
+          }
+        } 
+        draw.new <- na.omit(draw.new)
+        draw.parm <- rbind(draw.parm, draw.new)
+      }
+      if(iter > max.iter){
+        warning("Number of simulations lower than desired, consider increasing maximum iterations")
+      }
+    }
+  }
+  if (nrow(draw.parm) > num) 
+    draw.parm <- draw.parm[1:num,]
+  if (x$min.time==1 | x$min.time==2)
+    stop("Counterfactuals can only be specified from the third observation and later \n")
+  if (ncol(x$dta)==0){
+    ev <- matrix(NA, nrow=nrow(draw.parm), ncol=(pred.ahead))
+    se <- matrix(NA, nrow=nrow(draw.parm), ncol=(pred.ahead))
+    for (i in 1:nrow(draw.parm)){
+      temp <- arima(series, xreg=NULL,
+                    order=c(object$arma[1], object$arma[6], object$arma[2]), 
+                    seasonal=list(order=c(object$arma[3], object$arma[7], object$arma[4]),
+                      period=object$arma[5]), fixed=draw.parm[i,], transform.pars=TRUE)
+      temp2 <-predict(temp, newxreg=NULL, n.ahead=(pred.ahead))
+      ev[i,] <- temp2$pred[1:(pred.ahead)]
+      se[i,] <- temp2$se[1:(pred.ahead)]	
+    }
+    ev <- as.matrix(ev)
+    se <- as.matrix(se)
+    if (!is.null(x1)){
+      warning("First differences are only calculated when external regressors are used \n")
+    }
+  }
+  if (ncol(x$dta) > 0){
+    x.obs <- as.matrix(x$dta[1:(x$min.time-1),])
+    x.cf <- as.matrix(x$dta[x$min.time: (x$max.time), ])
+    ev <- matrix(NA, nrow=nrow(draw.parm), ncol=(length(x$min.time:x$max.time)))
+    se <- matrix(NA, nrow=nrow(draw.parm), ncol=(length(x$min.time:x$max.time)))
+    if (x$min.time == nrow(x$dta)){
+      x.cf<- t(x.cf)
+    }
+    for (i in 1:nrow(draw.parm)){
+      temp <- arima(series[1:(x$min.time-1)], xreg=x.obs,
+                    order=c(object$arma[1], object$arma[6], object$arma[2]), 
+                    seasonal=list(order=c(object$arma[3], object$arma[7], object$arma[4]),
+                      period=object$arma[5]), fixed=draw.parm[i,])
+      temp2 <-predict(temp, newxreg=x.cf, n.ahead=nrow(x.cf))
+      ev[i,] <- temp2$pred
+      se[i,] <- temp2$se
+    }
+    ev <- as.matrix(ev)
+  }
+  if (!is.null(x1)){
+    if (ncol(x1$dta) > 0){
+      x1.obs <- as.matrix(x1$dta[1:(x1$min.time-1), ])
+      x1.cf <- as.matrix(x1$dta[x1$min.time:(x1$max.time),])
+      ev.1 <- matrix(NA, nrow=nrow(draw.parm), ncol=length(x$min.time:x$max.time))
+      se.1 <- matrix(NA, nrow=nrow(draw.parm), ncol=length(x$min.time:x$max.time))
+      pred.ahead <- x$pred.ahead
+      pred.arima.x1 <- list()
+      false.arima.x1 <- list() 
+      if (x1$min.time == nrow(x1$dta)){
+	x1.cf <- t(x1.cf)
+      }
+      for (i in 1:nrow(draw.parm)){
+	temp3<- arima(series[1:(x1$min.time-1)], xreg=x1.obs,
+                                     order=c(object$arma[1], object$arma[6], object$arma[2]),
+                                    seasonal=list(order=c(object$arma[3], object$arma[7],
+                                                    object$arma[4]), period=object$arma[5]),
+                                     fixed=draw.parm[i,])
+	temp4 <- predict(temp3, newxreg=x1.cf, n.ahead=nrow(x.cf))
+	ev.1[i,] <- temp4$pred	
+	se.1[i,] <- temp4$se
+      }
+      ev.1 <- as.matrix(ev.1)
+    }
+  }
+  if (!is.null(x1) & t.effect){
+    warning("First differences and treatment effects are not both calculated.Calculating first differences only.\n")
+  }
+  if (is.null(x1) & (t.effect)){
+    t.eff <- matrix(NA, nrow=nrow(ev), ncol=ncol(ev))
+    for (i in 1:nrow(ev)){
+      t.eff[i,] <- series[(x$min.time):x$max.time] - ev[i,]
+    }
+  }
+  if (!is.null(x1) | !(t.effect)){
+    t.eff <- NULL
+  }
+  if (!is.null(x1)){
+    se <- as.matrix(se)
+    se.1 <- as.matrix(se.1)
+    fd <- ev.1 -  ev
+    qi <- list(ev=ev, se=se, fd=fd, t.eff=t.eff)
+    qi.name <- list(ev="Expected Values, E(Y|X)", se="Prediction Standard Error",
+                    fd="First Difference, E(Y|X1) - E(Y|X)", t.eff="Treatment Effect")
+    res <- list(min.time=x$min.time, qi=qi, qi.name=qi.name,
+                zelig.call = object$call, 
+                t.series = eval(eval(object$call$formula[[2]],
+                  envir=dat)$name, envir=dat))
+    class(res) <- c("zelig.arima", "zelig")
+    return(res)
+  } 
+  if (is.null(x1)){
+    se <- as.matrix(se)
+    qi <- list(ev=ev, se=se, t.eff=t.eff)
+    qi.name <- list(ev="Expected Values, E(Y|X)", se="Prediction Standard Error",
+                    t.eff="Treatment Effect") 
+    res <- list(min.time=x$min.time, qi=qi, qi.name=qi.name,
+                t.series=eval(eval(object$call$formula[[2]],
+                  envir=dat)$name, envir=dat),
+                zelig.call = object$call)
+    class(res) <- c("zelig.arima", "zelig")
+    return(res) 
+  } 
+} 
\ No newline at end of file
diff --git a/R/summary.Arima.R b/R/summary.Arima.R
new file mode 100644
index 0000000..b6d0566
--- /dev/null
+++ b/R/summary.Arima.R
@@ -0,0 +1,4 @@
+summary.Arima <- function(object, ...){
+  print(object, ...)
+}
+
diff --git a/R/summary.zelig.arima.R b/R/summary.zelig.arima.R
new file mode 100644
index 0000000..cc62b9c
--- /dev/null
+++ b/R/summary.zelig.arima.R
@@ -0,0 +1,57 @@
+summary.zelig.arima <- function(object, CI=95, stats=c("mean", "sd"),  ...){
+  if (is.null(object$qi$fd)){
+    if (is.null(object$qi$t.eff)){
+      test.array <- array(NA, c(ncol(object$qi[[1]]), length(stats)+2, 2))
+      for (i in 1:(length(object$qi)-1)){
+        for (j in 1:length(stats)){
+          test.array[,j, i]<- t(as.matrix(apply(object$qi[[i]], 2, stats[j])))
+        }
+        test.array[, c(length(stats) +1 , length(stats) + 2), i] <-
+          t(as.matrix(apply(object$qi[[i]], 2, "quantile",
+                            c((100-CI)/200, 1-(100-CI) / 200))))
+      }
+      colnames(test.array) <- c(stats, paste(as.character((100-CI)/200), "%", sep=""),
+                               paste(as.character(1-(100-CI)/200), "%", sep=""))
+      dimnames(test.array)[[3]] <- object$qi.name[1:2] 
+      rownames(test.array) <- (object$min.time+1):(object$min.time + nrow(test.array))  
+      number.sim <- nrow(object$qi$ev)
+    }
+    
+    if (!is.null(object$qi$t.eff)){
+      test.array <- array(NA, c(ncol(object$qi[[1]]), length(stats)+2, 3))
+      for (i in 1:length(object$qi)){
+        for (j in 1:length(stats)){
+          test.array[,j, i]<- t(as.matrix(apply(object$qi[[i]], 2, stats[j])))
+        }
+        test.array[, c(length(stats) +1 , length(stats) + 2), i] <-
+          t(as.matrix(apply(object$qi[[i]], 2, "quantile",
+                            c((100-CI)/200, 1-(100-CI)/200))))
+      }
+      colnames(test.array) <- c(stats, paste(as.character((100-CI)/200), "%", sep=""),
+                               paste(as.character(1-(100-CI)/200), "%", sep=""))
+      dimnames(test.array)[[3]] <- object$qi.name 
+      rownames(test.array) <- (object$min.time+1):(object$min.time + nrow(test.array))  
+      number.sim <- nrow(object$qi$ev)
+    }
+  }
+  if (!is.null(object$qi$fd)){
+    test.array <- array(NA, c(ncol(object$qi[[1]]), length(stats)+2, 3))
+    for (i in 1:(length(object$qi)-1)){
+      for (j in 1:length(stats)){
+        test.array[,j, i] <- t(as.matrix(apply(object$qi[[i]], 2, stats[j])))
+      }
+      test.array[, c(length(stats) +1 , length(stats) + 2), i] <-
+        t(as.matrix(apply(object$qi[[i]], 2, "quantile",
+                          c((100-CI)/200, 1-(100-CI)/200))))
+    }
+    colnames(test.array) <- c(stats, paste(as.character((100-CI)/200), "%", sep=""),
+                              paste(as.character(1-(100-CI)/200), "%", sep=""))
+    dimnames(test.array)[[3]] <- object$qi.name[1:3]
+    rownames(test.array) <- (object$min.time+1):(object$min.time + nrow(test.array)) 
+    number.sim <- nrow(object$qi$ev)   
+  }
+  out <- list(number.sim = number.sim, test.array = test.array,
+              zelig.call = object$zelig.call)
+  class(out) <- "arimaSummary"
+  out
+}
diff --git a/R/zelig.default.R b/R/zelig.default.R
index b10dfa6..34bf74c 100644
--- a/R/zelig.default.R
+++ b/R/zelig.default.R
@@ -52,15 +52,13 @@ zelig.default <- function(formula, model, data, by = NULL, save.data =
         d <- d[complete.cases(model.frame(mf$formula, data=d,
                                           na.action = na.pass)),]
         mf$data <- d
-        res <- eval(as.call(mf))
+        res <- create.ZeligS4(eval(as.call(mf)))
         if (exists(fn2)) 
           res <- do.call(fn2, list(res = res, fcall = mf,
                                    zcall = as.list(zelig.call)))
-        res$call <- zelig.call
+        res$call <- as.call(zelig.call)
         if (save.data)
-          res$data <- d
-        else
-          res$data <- res$call$data
+          res$zelig.data <- d
         res$zelig <- model
         if (M > 1) 
           obj[[j]] <- res
diff --git a/R/zelig2arima.R b/R/zelig2arima.R
new file mode 100644
index 0000000..ed82486
--- /dev/null
+++ b/R/zelig2arima.R
@@ -0,0 +1,70 @@
+zelig2arima <- function(formula, model, data, M, ...){
+  mf <- match.call()
+  ##arima.wrap allows arima to run without using a formula
+  mf[[1]] <- arima.wrap
+  ##assembling the order of the ARIMA model
+  dep.var <- eval(mf[[2]][[2]])$name
+  d <- eval(mf[[2]][[2]])$d
+  d.s <- eval(mf[[2]][[2]])$d.s
+  per <- eval(mf[[2]][[2]])$per
+  ##the don't use vector will be used below to assemble the actual formula employed
+  dont.use <- vector()
+  ##this for loop is used to find the values of p, p.s., q, and q.s.
+  n.par <- length(unlist(strsplit(deparse(mf[[2]][[3]]), "\\+")))
+  for(i in 1:n.par){
+    if(class(eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+                  envir=data))=="list"){
+      if(eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+              envir=data)$ep==TRUE){
+        dont.use[1] <- i
+        q <- eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+                  envir=data)$q
+        q.s <- eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+                    envir=data)$q.s
+      }
+      if(eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+              envir=data)$y==TRUE){
+        dont.use[2] <- i
+        p <- eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+                  envir=data)$p
+        p.s <- eval(parse(text=unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[i]),
+                    envir=data)$p.s
+      }
+    }
+  }
+  
+  if (length(dont.use) < n.par){
+    ##this vector then denotes what we are going to use on the right hand side
+    use.vec <- c(1:length(unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))))
+    use.vec <- use.vec[-dont.use]
+    ##now, we are creating the right hand side portion of the formula
+    rhs <- vector()
+    for (i in 1:length(use.vec)){
+      rhs[2*i-1] <- paste(unlist(strsplit(deparse(mf[[2]][[3]]), "\\+"))[use.vec[i]])
+      if (i+1 <= length(use.vec))
+        rhs[2*i] <- paste("+")
+      if (i + 1 > length(use.vec))
+       	break  
+    }
+    mf$formula <-formula <- as.formula(paste(paste(dep.var,"~", collapse=""),
+                                             paste(rhs, collapse=""), collapse=""))
+    D <- model.frame(mf$formula, data)
+    cols.D <- colnames(D)
+    mf$x <- D[,1]
+    X <- as.matrix(cbind(D[,2:ncol(D)]))
+    colnames(X) <- cols.D[2:length(cols.D)]
+    mf$xreg <- X
+  }
+  if(length(dont.use)==n.par){
+    mf$x <- as.matrix(eval(dep.var, envir=data))
+    mf$xreg <- NULL
+    mf$formula <- eval(dep.var, envir=data)~1 
+  }	
+  mf$order<- c(p,d,q) 
+  if(!is.null(d.s) & !is.null(p.s) & !is.null(q.s) & !is.null(per)){	
+    mf$seasonal$order <- c(p.s, d.s, q.s)
+    mf$seasonal$period <- per
+  }
+  mf$model <- mf$M <- mf$data <- NULL
+  as.call(mf) 
+}
diff --git a/data/approval.tab b/data/approval.tab
new file mode 100644
index 0000000..abc2ea1
--- /dev/null
+++ b/data/approval.tab
@@ -0,0 +1,66 @@
+"month" "year" "approve" "disapprove" "unsure" "sept.oct.2001" "iraq.war" "avg.price"
+"1" 2 2001 58.67 23.67 17.67 0 0 144.975
+"2" 3 2001 58 26.67 15.33 0 0 140.925
+"3" 4 2001 60.5 29.5 10 0 0 155.16
+"4" 5 2001 55 33.33 11.67 0 0 170.175
+"5" 6 2001 54 34 12 0 0 161.625
+"6" 7 2001 56.5 34 9.5 0 0 142.06
+"7" 8 2001 56 35 9 0 0 142.075
+"8" 9 2001 75.67 18.33 6 1 0 152.15
+"9" 10 2001 88 9 3 1 0 131.54
+"10" 11 2001 87 8.67 4.33 0 0 117.05
+"11" 12 2001 86 10.5 3.5 0 0 108.6
+"12" 1 2002 83.67 12.67 3.67 0 0 110.725
+"13" 2 2002 82 14 4 0 0 111.375
+"14" 3 2002 79.25 15.75 5 0 0 124.925
+"15" 4 2002 76.25 19 4.75 0 0 139.7
+"16" 5 2002 76.33 17.67 6 0 0 139.175
+"17" 6 2002 73.4 20.2 6.4 0 0 138.225
+"18" 7 2002 70.5 23.5 6 0 0 139.7
+"19" 8 2002 66.5 27 6.5 0 0 139.575
+"20" 9 2002 67.2 28.2 4.6 0 0 139.96
+"21" 10 2002 64.75 29 6.25 0 0 144.525
+"22" 11 2002 66.33 27 6.67 0 0 141.9
+"23" 12 2002 62.75 31.5 5.75 0 0 138.58
+"24" 1 2003 60.17 35 4.83 0 0 145.75
+"25" 2 2003 58.75 35.75 5.5 0 0 161.3
+"26" 3 2003 65.2 30.6 4.2 0 1 169.3
+"27" 4 2003 70 25.75 4.25 0 1 158.9
+"28" 5 2003 66.33 30 3.67 0 1 149.725
+"29" 6 2003 62 34.33 3.67 0 0 149.28
+"30" 7 2003 59.67 36.67 3.67 0 0 151.25
+"31" 8 2003 59.5 36.5 4 0 0 162.025
+"32" 9 2003 51 45 4 0 0 167.88
+"33" 10 2003 54.67 41.33 4 0 0 156.35
+"34" 11 2003 51.67 45 3.33 0 0 151.2
+"35" 12 2003 58.5 38.25 3.25 0 0 147.88
+"36" 1 2004 55.25 41.25 3.5 0 0 157.175
+"37" 2 2004 51.33 45.33 3.33 0 0 164.75
+"38" 3 2004 50.67 46.33 3 0 0 173.6
+"39" 4 2004 52 45 3 0 0 179.775
+"40" 5 2004 47.33 49.33 3.33 0 0 198.34
+"41" 6 2004 48.5 49 2.5 0 0 196.925
+"42" 7 2004 47.75 48.5 3.75 0 0 191.125
+"43" 8 2004 50 46.5 3.5 0 0 187.8
+"44" 9 2004 52.67 45 2.33 0 0 186.975
+"45" 10 2004 49.17 47.67 3.17 0 0 199.95
+"46" 11 2004 54 43 3 0 0 197.94
+"47" 12 2004 51 45 4 0 0 184.1
+"48" 1 2005 51.67 44.67 3.67 0 0 183.08
+"49" 2 2005 52.25 44.5 3.25 0 0 191
+"50" 3 2005 49.67 45.67 4.67 0 0 207.925
+"51" 4 2005 48.5 47.75 3.75 0 0 224.25
+"52" 5 2005 48 47.33 4.67 0 0 216.12
+"53" 6 2005 46.25 51 2.75 0 0 215.55
+"54" 7 2005 47.33 49 3.67 0 0 229
+"55" 8 2005 43.75 52.5 3.75 0 0 248.62
+"56" 9 2005 44 52.75 3.25 0 0 290.325
+"57" 10 2005 40.75 56.25 3 0 0 271.68
+"58" 11 2005 38.33 57.33 4.33 0 0 225.675
+"59" 12 2005 42.25 54 3.75 0 0 218.5
+"60" 1 2006 43 53.67 3.67 0 0 231.56
+"61" 2 2006 39.67 57 3.33 0 0 228
+"62" 3 2006 36.5 59.5 4.5 0 0 242.475
+"63" 4 2006 35.67 60.67 3.67 0 0 274.2
+"64" 5 2006 37 57.67 5 0 0 290.68
+"65" 6 2006 40 55 5 0 0 288.45
diff --git a/demo/00Index b/demo/00Index
index 264ebe3..9c67be1 100644
--- a/demo/00Index
+++ b/demo/00Index
@@ -1,3 +1,4 @@
+arima              Arima models
 beta               Beta regression and simulation
 blogit             Bivariate Logit regression and simulation 
 bivariate.probit   New model example: Bivariate probit MLE
diff --git a/demo/arima.R b/demo/arima.R
new file mode 100644
index 0000000..a81bde5
--- /dev/null
+++ b/demo/arima.R
@@ -0,0 +1,75 @@
+#### Example 1: No External Regressors ####
+data(approval)
+
+# Estimate the ARIMA model, and summarize the results
+
+z.out1<- zelig(Diff(approve, 1)~lag.eps(2) + lag.y(2), data=approval, model="arima")
+summary(z.out1)
+user.prompt()
+
+# Set the number of time periods ahead for the prediction to run
+
+
+x.out1<- setx(z.out1, pred.ahead=10)
+
+
+
+# Simulate the predicted quantities of interest
+user.prompt()
+
+s.out1<- sim(z.out1, x=x.out1)
+
+# summarize and plot the results
+user.prompt()
+
+summary(z.out1)
+plot(s.out1, lty.set=2)
+
+#### Example 2: External Regressors, 1 Counterfactual, 1 Time Period ####
+
+# Estimates an ARIMA model where we include exogenous regressors 
+# in addition to lagged errors and lagged values of the dependent variable.  
+# This example shows the output if only one counterfactual value is specified for
+# a time period.  
+
+z.out2<- zelig(Diff(approve, 1)~ iraq.war + sept.oct.2001 + avg.price + lag.eps(1) + lag.y(2),
+               data=approval, model="arima")
+
+# Set the both the value and time period of counterfactual of interest.
+user.prompt()
+x.out2<- setx(z.out2, sept.oct.2001=list(time=45, c.fact=0), cond=T)
+
+# Simulate the quantities of interest
+user.prompt()
+
+s.out2<-sim(z.out2, x=x.out2) 
+
+# Summarizing and plotting the quantities of interest
+
+user.prompt()
+
+summary(s.out2)
+plot(s.out2)
+
+
+
+#### Example 3: External Regressors, Counterfactuals Over Many Time Periods ####
+
+# This example continues to use the same model specification as above, but will show
+# the output when several counterfactual values are specified.  
+
+user.prompt()
+
+x.out3<- setx(z.out2, sept.oct.2001=list(time=45:50, c.fact=0))
+x1.out3<- setx(z.out2, sept.oct.2001=list(time=45:50, c.fact=1))
+
+# Simulating the quantities of interest
+
+user.prompt()
+
+s.out3<- sim(z.out2, x=x.out3, x1=x1.out3)
+# Summarizing and plotting the quantities of interest.  Here we are 
+# only displaying the uncertainty resulting from parameter estimation  
+
+summary(s.out3)
+plot(s.out3, pred.se=FALSE)
diff --git a/demo/bivariate.probit.R b/demo/bivariate.probit.R
index 4a67865..218159d 100644
--- a/demo/bivariate.probit.R
+++ b/demo/bivariate.probit.R
@@ -2,11 +2,11 @@ describe.bivariate.probit <- function() {
   category <- "bivaraite.dichotomous"
   package <- list(name = "mvtnorm", 
                   version = "0.7")
-  mu <- list(equations = 2,               # Systematic component 
+  mu <- list(equations = c(2,2),               # Systematic component 
              tagsAllowed = TRUE,          
              depVar = TRUE, 
              expVar = TRUE)
-  rho <- list(equations = 1,              # Optional systematic component
+  rho <- list(equations = c(1,1),              # Optional systematic component
              tagsAllowed = FALSE,         #   Estimated as an ancillary
              depVar = FALSE,              #   parameter by default
              expVar = TRUE)
diff --git a/inst/doc/.latex2html-init b/inst/doc/.latex2html-init
new file mode 100644
index 0000000..92e6b95
--- /dev/null
+++ b/inst/doc/.latex2html-init
@@ -0,0 +1,244 @@
+#LaTeX2HTML Version 96.1 : dot.latex2html-init
+#
+### Command Line Argument Defaults #######################################
+
+$MAX_SPLIT_DEPTH = 8;	# Stop making separate files at this depth
+
+$MAX_LINK_DEPTH = 4;    # Stop showing child nodes at this depth   
+
+$NOLATEX = 0;           # 1 = do not pass unknown environments to Latex
+
+$EXTERNAL_IMAGES = 0;   # 1 = leave the images outside the document 
+
+$ASCII_MODE = 0;        # 1 = do not use any icons or internal images
+
+# 1 =  use links to external postscript images rather than inlined bitmap
+# images.
+$PS_IMAGES = 0;
+
+$TITLE = $default_title;      # The default is "No Title" 
+
+# $STYLESHEET_CASCADE = "<link rel='stylesheet' href='/gking.css' MEDIA='screen' TYPE='text/css'>";
+
+# add new UI Javascript to HEAD section. -rmesard 30-nov-2005
+# $LATEX2HTML_META = "<script language='Javascript' src='/gking.js'></script>";
+
+$DESTDIR = '';         # Put the result in this directory 
+
+# When this is set, the generated HTML files will be placed in the 
+# current directory. If set to 0 the default behaviour is to create (or reuse)
+# another file directory.
+$NO_SUBDIR = 0;
+
+
+# Supply your own string if you don't like the default <Name> <Date>
+$ADDRESS = "Gary King \n$address_data[1]<script language='Javascript'>displayFooter()</script>";
+
+$NO_NAVIGATION = 0;	# 1 = do not put a navigation panel at the top of each page
+
+# Put navigation links at the top of each  page.  If  the page  exceeds
+# $WORDS_IN_PAGE  number of words then put one at the bottom of the page.
+$AUTO_NAVIGATION = 0;
+
+# Put a link to the index page in  the  navigation  panel
+$INDEX_IN_NAVIGATION = 1;
+
+# Put a link to the table of contents  in  the  navigation  panel
+$CONTENTS_IN_NAVIGATION = 1;
+
+# Put a link to the next logical page  in  the  navigation  panel
+$NEXT_PAGE_IN_NAVIGATION = 1;
+
+# Put a link to the previous logical page  in  the  navigation  panel
+$PREVIOUS_PAGE_IN_NAVIGATION = 1;
+
+$INFO = 1;              # 0 = do not make a "About this document..." section 
+
+# Reuse images generated during previous runs
+$REUSE = 2;
+
+# When this is 1, the section numbers are shown. The section numbers should 
+# then match those that would have bee produced by LaTeX.
+# The correct section numbers are obtained from the $FILE.aux file generated 
+# by LaTeX.
+# Hiding the seciton numbers encourages use of particular sections 
+# as standalone documents. In this case the cross reference to a section 
+# is shown using the default symbol rather than the section number.
+$SHOW_SECTION_NUMBERS = 0;
+
+### Other global variables ###############################################
+$CHILDLINE = "<BR> <HR>\n";
+
+# This is the line width measured in pixels and it is used to right justify
+# equations and equation arrays; 
+$LINE_WIDTH = 500;		
+
+# Used in conjunction with AUTO_NAVIGATION
+$WORDS_IN_PAGE = 300;	
+
+# Affects ONLY the way accents are processed 
+$default_language = 'english';	
+#$default_language = 'japanese';	
+
+# The value of this variable determines how many words to use in each 
+# title that is added to the navigation panel (see below)
+# 
+$WORDS_IN_NAVIGATION_PANEL_TITLES = 4;
+
+# This number will determine the size of the equations, special characters,
+# and anything which will be converted into an inlined image
+# *except* "image generating environments" such as "figure", "table" 
+# or "minipage".
+# Effective values are those greater than 0.
+# Sensible values are between 0.1 - 4.
+$MATH_SCALE_FACTOR = 1.6;
+
+# This number will determine the size of 
+# image generating environments such as "figure", "table" or "minipage".
+# Effective values are those greater than 0.
+# Sensible values are between 0.1 - 4.
+$FIGURE_SCALE_FACTOR = 1.6;
+
+
+#  If this is set then intermediate files are left for later inspection.
+#  This includes $$_images.tex and $$_images.log created during image
+#  conversion.
+#  Caution: Intermediate files can be *enormous*.
+$DEBUG = 0;
+
+#  If both of the following two variables are set then the "Up" button
+#  of the navigation panel in the first node/page of a converted document
+#  will point to $EXTERNAL_UP_LINK. $EXTERNAL_UP_TITLE should be set
+#  to some text which describes this external link.
+$EXTERNAL_UP_LINK = "";
+$EXTERNAL_UP_TITLE = "";
+
+# If this is set then the resulting HTML will look marginally better if viewed 
+# with Netscape.
+$NETSCAPE_HTML = 0;
+
+# Valid paper sizes are "letter", "legal", "a4","a3","a2" and "a0"
+# Paper sizes has no effect other than in the time it takes to create inlined
+# images and in whether large images can be created at all ie
+#  - larger paper sizes *MAY* help with large image problems 
+#  - smaller paper sizes are quicker to handle
+$PAPERSIZE = "a4";
+
+# Replace "english" with another language in order to tell LaTeX2HTML that you 
+# want some generated section titles (eg "Table of Contents" or "References")
+# to appear in a different language. Currently only "english" and "french"
+# is supported but it is very easy to add your own. See the example in the
+# file "latex2html.config" 
+$TITLES_LANGUAGE = "english";
+
+### Navigation Panel ##########################################################
+#
+# The navigation panel is constructed out of buttons and section titles.
+# These can be configured in any combination with arbitrary text and 
+# HTML tags interspersed between them. 
+# The buttons available are:
+# $PREVIOUS - points to the previous section
+# $UP  - points up to the "parent" section
+# $NEXT - points to the next section
+# $NEXT_GROUP - points to the next "group" section
+# $PREVIOUS_GROUP - points to the previous "group" section
+# $CONTENTS - points to the contents page if there is one
+# $INDEX - points to the index page if there is one
+#
+# If the corresponding section exists the button will contain an
+# active link to that section. If the corresponding section does
+# not exist the button will be inactive.
+#
+# Also for each of the $PREVIOUS $UP $NEXT $NEXT_GROUP and $PREVIOUS_GROUP
+# buttons there are equivalent $PREVIOUS_TITLE, $UP_TITLE, etc variables
+# which contain the titles of their corresponding sections. 
+# Each title is empty if there is no corresponding section.
+#
+# The subroutine below constructs the navigation panels in each page.
+# Feel free to mix and match buttons, titles, your own text, your logos,
+# and arbitrary HTML (the "." is the Perl concatenation operator).
+sub top_navigation_panel {
+
+    # call script func from gking.js to open new UI 
+    # this should be the 1st thing after BODY tag -rmesard 30-nov-2005
+    "<script language='Javascript'>displayHeader()</script>\n" .
+
+    # Now add a few buttons with a space between them
+    # "$NEXT $UP $PREVIOUS $CONTENTS $INDEX $CUSTOM_BUTTONS" .
+    
+    # "<BR>\n" .		# Line break
+      "<table border='0' cellpadding='0' cellspacing='0' class='latexnav'>" .
+      "<tr><td class='latexnavhome'>" .
+      "<a href='/'>" .
+#      "<img src='/images/gking_name_sm.gif' height='30' width='119' border='0' alt='Gary King Homepage' />" .
+      "</a></td>" .
+	
+   # try for the contents link
+   # ($CONTENTS_TITLE ? "<td>c: $CONTENTS_TITLE </td>\n" : undef) .
+     
+   # ... and the ``previous'' title
+    ($PREVIOUS_TITLE ? "<td> Previous: $PREVIOUS_TITLE </td>\n" : undef) .
+
+    # Similarly with the ``up'' title ...
+    ($UP_TITLE ? "<td> Up: $UP_TITLE </td>\n" : undef) . 
+
+    # If ``next'' section exists, add its title to the navigation panel
+    ($NEXT_TITLE ? "<td> Next: $NEXT_TITLE </td>\n" : undef) . 
+    
+ 
+   
+    #  Line Break, horizontal rule (3-d dividing line) and new paragraph  
+    "</tr></table>\n" .
+    "<BR> <P>\n"		
+}
+
+sub bot_navigation_panel {
+
+    #  Start with a horizontal rule (3-d dividing line)
+    "<HR>"			
+    
+    # Now add a few buttons with a space between them
+    # "$NEXT $UP $PREVIOUS $CONTENTS $INDEX $CUSTOM_BUTTONS" .
+    
+    # "<BR>\n" .		# Line break
+	
+    # If ``next'' section exists, add its title to the navigation panel
+    # ($NEXT_TITLE ? "<B> Next:</B> $NEXT_TITLE\n" : undef) . 
+    
+    # Similarly with the ``up'' title ...
+    # ($UP_TITLE ? "<B>Up:</B> $UP_TITLE\n" : undef) . 
+ 
+    # ... and the ``previous'' title
+    # ($PREVIOUS_TITLE ? "<B> Previous:</B> $PREVIOUS_TITLE\n" : undef)
+
+}
+
+sub meta_information {
+    local($_) = @_;
+    # Cannot have nested HTML tags...
+    do { s/<[^>]*>//g;
+        "<SCRIPT LANGUAGE=\"Javascript\" SRC=\"/gking.js\"></SCRIPT>\n" .
+        "<META NAME=\"description\" CONTENT=\"$_\">\n" .
+        "<META NAME=\"keywords\" CONTENT=\"$FILE\">\n" .
+        "<META NAME=\"resource-type\" CONTENT=\"document\">\n" .
+        "<META NAME=\"distribution\" CONTENT=\"global\">\n"
+    } if $_;
+} 
+
+ our(%custom_filenames);
+ sub custom_title_hook {
+ my($sec_name) = shift;
+   $sec_name=~tr/[a-z][A-Z][0-9] /_/sc;
+   my(@words)= split(/\s+/,$sec_name);
+   my($fn)= join("_", at words[0..2]);
+   $fn=substr($fn,0,22);
+   $fn=~s/(.*?)_*$/$1/;
+   $custom_filenames{"$fn"}++;
+   if ($custom_filenames{"$fn"}>1) {
+        $fn=$fn . $custom_filenames{"$fn"};
+   }
+   return($fn);
+ }
+ $CUSTOM_TITLES=1;
+
+1;	# This must be the last line
diff --git a/inst/doc/Makefile b/inst/doc/Makefile
new file mode 100644
index 0000000..b9d129e
--- /dev/null
+++ b/inst/doc/Makefile
@@ -0,0 +1,16 @@
+    
+# stub makefile, does nothing, but causes R CMD build not to force rebuild pdf file
+
+REVAL=TRUE
+
+all:	Zelig/inst/doc/zelig.pdf
+
+%.tex:	%.Rnw
+	echo "Sweave("$<", debug=TRUE, eval=$(REVAL))" | R --slave
+
+%.pdf:	%.tex
+	pdflatex $*
+	bibtex $*
+	pdflatex $*
+	pdflatex $*
+	pdflatex $*
diff --git a/inst/doc/Zcommands.tex b/inst/doc/Zcommands.tex
new file mode 100644
index 0000000..a26c5ad
--- /dev/null
+++ b/inst/doc/Zcommands.tex
@@ -0,0 +1,767 @@
+\chapter{Statistical Commands}
+
+\section{Zelig Commands}\label{s:commands}
+  
+\subsection{Quick Overview}\label{overview}
+  
+For any statistical model, Zelig does its work with a combination of
+three commands.  
+
+\begin{figure}[h!]
+\caption{Main Zelig commands (solid arrows) and some options (dashed arrows)}
+\label{3steps}
+\begin{center}
+\begin{displaymath}
+\SelectTips{eu}{12} \UseTips 
+\xymatrix@!C{\quad {\rm Imputation}
+\ar@{-->}[dr] & & {\rm Matching} \ar@{-->}[dl] \\ 
+ & *+[F-:<10pt>]{{\tt zelig()}} \ar@{->}[d] \ar@{-->}[r] &
+{\tt summary()}\\ 
+ & *+[F-:<10pt>]{{\tt setx()}}  \ar@{-->}[r] \ar@{->}[d] & {\tt whatif()}\\ 
+& *+[F-:<10pt>]{{\tt sim()}} \ar@{-->}[dr] \ar@{-->}[dl] & \\
+{\tt summary()} & &{\tt plot()} } 
+\end{displaymath}
+\end{center}
+\end{figure}
+
+\begin{enumerate}
+\item Use \texttt{zelig()} to run the chosen statistical model on a
+given data set, with a specific set of variables.  For standard
+likelihood models, for example, this step estimates the coefficients,
+other model parameters, and a variance-covariance matrix.  In
+addition, you may choose from a variety of options:
+\begin{itemize} 
+\item Pre-processing:  Prior to calling {\tt zelig()}, you may
+  choose from a variety of data pre-processing commands (matching or
+  multiple imputation, for example) to make your statistical
+  inferences more accurate. 
+\item Summarize model: After calling {\tt zelig()}, you may summarize
+the regression output using {\tt summary()}.  
+\end{itemize}
+\item Use \texttt{setx()} to set each of the explanatory variables to
+  chosen (actual or counterfactual) values in preparation for
+  calculating quantities of interest.  After calling {\tt setx()}, you
+  may use \hlink{WhatIf}{/whatif/} to evaluate these choices by
+  determining whether they involve interpolation (i.e., are inside the
+  convex hull of the observed data) or extrapolation, as well as how
+  far these counterfactuals are from the data.  Counterfactuals chosen
+  in \texttt{setx()} that involve extrapolation far from the data can
+  generate considerably more model dependence (see \cite{KinZen06a},
+  \cite{KinZen06b}, \cite{StoKinZen06}).
+\item Use \texttt{sim()} to draw simulations of your quantity of
+  interest (such as a predicted value, predicted probability, risk
+  ratio, or first difference) from the model.  (These simulations may
+  be drawn using an asymptotic normal approximation (the default),
+  bootstrapping, or other methods when available, such as directly
+  from a Bayesian posterior.)  After calling {\tt sim()}, use any of
+the following to summarize the simulations:
+\begin{itemize}
+\item The {\tt summary()} function gives a numerical display. For
+multiple {\tt setx()} values, {\tt summary()} lets you summarize
+simulations by choosing one or a subset of observations.
+\item If the {\tt setx()} values consist of only one observation, {\tt
+plot()} produces density plots for each quantity of interest.  
+\end{itemize}
+\end{enumerate}
+Whenever possible, we use {\tt z.out} as the {\tt zelig()} output
+object, \texttt{x.out} as the \texttt{setx()} output object, and {\tt
+s.out} as the {\tt sim()} output object, but you may choose other
+names.
+
+\subsection{Examples}
+\label{mi.ex}
+\begin{itemize}
+\item Use the \texttt{turnout} data set included with Zelig to
+  estimate a logit model of an individual's probability of voting as
+  function of race and age. Simulate the predicted probability of
+  voting for a white individual, with age held at its mean:
+\begin{verbatim}
+> data(turnout)
+> z.out <- zelig(vote ~ race + age, model = "logit", data = turnout)
+> x.out <- setx(z.out, race = "white")
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{verbatim} 
+\item Compute a first difference and risk ratio, changing education
+  from 12 to 16 years, with other variables held at their means in the
+  data:
+\begin{verbatim}
+> data(turnout)
+> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+> x.low <- setx(z.out, educate = 12)
+> x.high <- setx(z.out, educate = 16)
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)                                     # Numerical summary.
+> plot(s.out)                                        # Graphical summary.
+\end{verbatim}
+\item Calculate expected values for every observation in your
+  data set:
+\begin{verbatim}
+> data(turnout)
+> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+> x.out <- setx(z.out, fn = NULL)
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{verbatim} 
+
+\item Use five multiply imputed data sets from \cite{SchSla01} in an
+  ordered logit model:
+\begin{verbatim}
+> data(immi1, immi2, immi3, immi4, immi5)
+> z.out <- zelig(as.factor(ipip) ~ wage1992 + prtyid + ideol,
+                 model = "ologit",
+                 data = mi(immi1, immi2, immi3, immi4, immi5))
+\end{verbatim}
+
+\item Use the nearest propensity score matching via {\it MatchIt}
+  package, and then calculate the conditional average treatment effect
+  of the job training program based on the linear regression model:
+\begin{verbatim}
+> library(MatchIt)
+> data(lalonde)
+> m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, 
+                   data = lalonde, method = "nearest")
+> m.data <- match.data(m.out)
+> z.out <- zelig(re78 ~ treat + distance + re74 + re75 + educ + black +
+                 hispan + age, data = m.data, model = "ls")
+> x.out0 <- setx(z.out, fn = NULL, treat = 0)
+> x.out1 <- setx(z.out, fn = NULL, treat = 1)
+> s.out <- sim(z.out, x=x.out0, x1=x.out1)
+> summary(s.out)
+\end{verbatim}
+
+\end{itemize}
+
+\subsection{Details}
+
+\begin{enumerate}
+\item {\tt z.out <- zelig(formula, model, data, by = NULL, \dots)}
+ 
+  The {\tt zelig()} command estimates a selected statistical model
+  given the specified data.  You may name the output object
+  (\texttt{z.out} above) anything you desire.  You must include three
+  required arguments, in the following order:
+
+  \begin{enumerate}
+
+  \item \texttt{formula} takes the form {\tt y \~\, x1 + x2}, where
+    {\tt y} is the dependent variable and {\tt x1} and {\tt x2} are
+    the explanatory variables, and {\tt y}, {\tt x1}, and {\tt x2} are
+    contained in the same dataset.  The {\tt +} symbol means
+    ``inclusion'' not ``addition.''  You may include interaction terms
+    in the form of {\tt x1*x2} without having to compute them in prior
+    steps or include the main effects separately.  For example, R
+    treats the formula {\tt y \~\, x1*x2} as {\tt y \~\, x1 + x2 +
+      x1*x2}.  To prevent R from automatically including the separate
+    main effect terms, use the {\tt I()} function, thus: {\tt y \~\,
+      I(x1 * x2)}.
+
+  \item \texttt{model} lets you choose which statistical model to run.
+    You must put the name of the model in quotation marks, in the form
+    {\tt model = "ls"}, for example.  See \Sref{s:models} for a list
+    of currently supported models.
+
+  \item \texttt{data} specifies the data frame containing the
+    variables called in the formula, in the form {\tt data = mydata}.
+    Alternatively, you may input multiply imputed datasets in the form
+    {\tt data = mi(data1, data2, \dots)}.\footnote{Multiple
+      imputation is a method of dealing with missing values in your
+      data which is more powerful than the usual list-wise deletion
+      approach.  You can create multiply imputed datasets with a
+      program such as
+      \hlink{Amelia}{http://gking.harvard.edu/stats.shtml\#amelia};
+      see \hlink{King, Honaker, Joseph, Scheve
+        (2000)}{http://gking.harvard.edu/files/abs/evil-abs.shtml}.
+    \label{mi}}  If you are working with matched data created using
+  \hlink{MatchIt}{http://gking.harvard.edu/matchit/}, you may create a
+  data frame within the {\tt zelig()} statement by using {\tt data =
+    match.data(\dots)}.  In all cases, the data frame or MatchIt
+  object must have been previously loaded into the working memory.
+  
+\item {\tt by} (an optional argument which is by default {\tt NULL})
+  allows you to choose a factor variable (see \Sref{factors}) in the
+  data frame as a subsetting variable.  For each of the unique
+  strata defined in the {\tt by} variable, {\tt zelig()} does a
+  separate run of the specified model.  The variable chosen should
+  \emph{not} be in the formula, because there will be no variance in
+  the {\tt by} variable in the subsets.  If you have one data set for
+  all 191 countries in the UN, for example, you may use the {\tt by}
+  option to run the same model 191 times, once on each country, all
+  with a single {\tt zelig()} statement.  You may also use the {\tt
+    by} option to run models on
+  \hlink{MatchIt}{http://gking.harvard.edu/matchit/} subclasses.
+  
+\item The output object, {\tt z.out}, contains all of the options
+  chosen, including the name of the data set.  Because data sets may
+  be large, Zelig does not store the full data set, but only the name
+  of the dataset.  Every time you use a Zelig function, it looks for
+  the dataset with the appropriate name in working memory.  (Thus, it
+  is critical that you do \emph{not} change the name of your data set,
+  or perform any additional operations on your selected variables
+  between calling {\tt zelig()} and {\tt setx()}, or between {\tt
+    setx()} and {\tt sim()}.)
+  
+\item If you would like to view the regression output at this
+  intermediate step, type {\tt summary(z.out)} to return the
+  coefficients, standard errors, $t$-statistics and $p$-values.  We
+  recommend instead that you calculate quantities of interest;
+  creating {\tt z.out} is only the first of three steps in this task.
+  \end{enumerate}
+  
+\item {\tt x.out <- setx(z.out, fn = list(numeric = mean, ordered =
+    median, others = mode), data = NULL, cond = FALSE, \dots)}
+ 
+  The {\tt setx()} command lets you choose values for the explanatory
+  variables, with which {\tt sim()} will simulate quantities of
+  interest.  There are two types of {\tt setx()} procedures:
+\begin{itemize}
+\item You may perform the usual \emph{unconditional} prediction (by
+  default, {\tt cond = FALSE}), by explicitly choosing the values of
+  each explanatory variable yourself or letting {\tt setx()} compute
+  them, either from the data used to create {\tt z.out} or from a new
+  data set specified in the optional {\tt data} argument.  You may
+  also compute predictions for all observed values of your explanatory
+  variables using {\tt fn = NULL}.
+  
+\item Alternatively, for advanced uses, you may perform
+  \emph{conditional} prediction ({\tt cond = TRUE}), which predicts
+  certain quantities of interest by conditioning on the observed value
+  of the dependent variable.  In a simple linear regression model,
+  this procedure is not particularly interesting, since the
+  conditional prediction is merely the observed value of the dependent
+  variable for that observation.  However, conditional prediction is
+  extremely useful for other models and methods, including the
+  following:
+  \begin{itemize}
+  \item In a matched sampling design, the sample average treatment
+    effect for the treated can be estimated by computing the
+    difference between the observed dependent variable for the treated
+    group and their expected or predicted values of the dependent
+    variable under no treatment \citep{HoImaKin05}.
+  \item With censored data, conditional prediction will ensure that
+    all predicted values are greater than the censored observed
+    values \citep{KinAltBur90}.  
+  \item In ecological inference models, conditional prediction
+    guarantees that the predicted values are on the tomography line
+    and thus restricted to the known bounds
+    \citep{King97,AdoKinHer03}.
+  \item The conditional prediction in many linear random effects (or
+  Bayesian hierarchical) models is a weighted average of the
+  unconditional prediction and the value of the dependent variable for
+  that observation, with the weight being an estimable function of the
+  accuracy of the unconditional prediction \citep[see][]{GelKin94a}.
+  When the unconditional prediction is highly certain, the weight on
+  the value of the dependent variable for this observation is very
+  small, hence reducing inefficiency; when the unconditional
+  prediction is highly uncertain, the relative weight on the
+  unconditional prediction is very small, hence reducing bias.
+  Although the simple weighted average expression no longer holds in
+  nonlinear models, the general logic still holds and the mean square
+  error of the measurement is typically reduced
+  \citep[see][]{KinMurSal04}.
+  \end{itemize}
+  In these and other models, conditioning on the observed value of the
+  dependent variable can vastly increase the accuracy of prediction
+  and measurement.  
+\end{itemize}
+  
+  The {\tt setx()} arguments for \textbf{unconditional} prediction are
+  as follows:
+
+   \begin{enumerate}
+
+   \item {\tt z.out}, the {\tt zelig()} output object, must be
+     included first.
+
+   \item You can set particular explanatory variables to specified
+   values.  For example:
+\begin{verbatim}
+> z.out <- zelig(vote ~ age + race, model = "logit", data = turnout)
+> x.out <- setx(z.out, age = 30)
+\end{verbatim}
+   \texttt{setx()} sets the variables \emph{not} explicitly listed to
+   their mean if numeric, and their median if ordered factors, and
+   their mode if unordered factors, logical values, or character
+   strings.  Alternatively, you may specify one explanatory variable
+   as a range of values, creating one observation for every unique
+   value in the range of values:\footnote{If you allow more than one variable to 
+vary at a time, you risk confounding the predictive effect of the variables in 
+question.} \begin{verbatim}
+> x.out <- setx(z.out, age = 18:95)   
+\end{verbatim}
+   This creates 78 observations with with age set to 18 in the first
+   observation, 19 in the second observation, up to 95 in the 78th
+   observation.  The other variables are set to their default values,
+   but this may be changed by setting {\tt fn}, as described next.
+   
+ \item Optionally, \texttt{fn} is a list which lets you to choose a different
+   function to apply to explanatory variables of class
+  \begin{itemize} 
+    \item {\tt numeric}, which is {\tt mean} by default,
+    \item {\tt ordered} factor, which is {\tt median} by default, and
+    \item {\tt other} variables, which consist of logical variables,
+      character string, and unordered factors, and are set to their
+      {\tt mode} by default.
+  \end{itemize}
+  While any function may be applied to numeric variables, {\tt mean}
+  will default to median for ordered factors, and mode is the only
+  available option for other types of variables. In the special case,
+  {\tt fn = NULL}, {\tt setx()} returns all of the observations.
+   
+ \item You cannot perform other math operations within the {\tt fn}
+   argument, but can use the output from one call of {\tt setx} to
+   create new values for the explanatory variables.  For example, to
+   set the explanatory variables to one standard deviation below their
+   mean:
+\begin{verbatim}
+> X.sd <- setx(z.out, fn = list(numeric = sd))     
+> X.mean <- setx(z.out, fn = list(numeric = mean)) 
+> x.out <- X.mean - X.sd           
+\end{verbatim}
+   
+ \item Optionally, \texttt{data} identifies a new data frame (rather
+   than the one used to create {\tt z.out}) from which the {\tt
+     setx()} values are calculated.  You can use this argument to set
+   values of the explanatory variables for hold-out or out-of-sample
+   fit tests.
+ \item The {\tt cond} is always {\tt FALSE} for unconditional
+   prediction.
+ \end{enumerate}
+   
+ If you wish to calculate risk ratios or first differences, call {\tt
+   setx()} a second time to create an additional set of the values for
+ the explanatory variables.  For example, continuing from the example
+ above, you may create an alternative set of explanatory variables
+ values one standard deviation above their mean:
+\begin{verbatim}
+> x.alt <- X.mean + X.sd
+\end{verbatim}
+ 
+ The required arguments for \textbf{conditional} prediction are as
+ follows:
+ \begin{enumerate}
+ \item {\tt z.out}, the {\tt zelig()} output object, must be included
+   first.
+   
+ \item {\tt fn}, which equals {\tt NULL} to indicate that all of the
+   observations are selected.  You may only perform conditional
+   inference on actual observations, not the mean of observations or
+   any other function applied to the observations.  Thus, if {\tt fn}
+   is missing, but {\tt cond = TRUE}, {\tt setx()} coerces {\tt fn =
+     NULL}.
+
+ \item {\tt data}, the data for conditional prediction.  
+   
+ \item {\tt cond}, which equals {\tt TRUE} for conditional prediction.
+ \end{enumerate}
+ Additional arguments, such as any of the variable names, are ignored
+ in conditional prediction since the actual values of that observation
+ are used.
+ 
+\item {\tt s.out <- sim(z.out, x = x.out, x1 = NULL, num = c(1000,
+    100), bootstrap = FALSE, bootfn = NULL, \dots)}
+ 
+  The {\tt sim()} command simulates quantities of interest given the
+  output objects from \texttt{zelig()} and \texttt{setx()}.  This
+  procedure uses only the assumptions of the statistical model.  The
+  {\tt sim()} command performs either unconditional or conditional
+  prediction depending on the options chosen in {\tt setx()}.
+  
+  The arguments are as follows for \textbf{unconditional} prediction:
+  \begin{enumerate}
+  \item {\tt z.out}, the model output from {\tt zelig()}.
+  \item {\tt x}, the output from the {\tt setx()} procedure performed
+    on the model output.
+  \item Optionally, you may calculate first differences by specifying
+    {\tt x1}, an additional {\tt setx()} object.  For example, using
+    the {\tt x.out} and {\tt x.alt}, you may generate first
+    differences using:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out, x1 = x.alt)
+\end{verbatim}
+    
+  \item By default, the number of simulations, {\tt num}, equals 1000
+    (or 100 simulations if bootstrap is selected), but this may be
+    decreased to increase computational speed, or increased for
+    additional precision.
+    
+  \item Zelig simulates parameters from classical \emph{maximum
+      likelihood} models using asymptotic normal approximation to the
+    log-likelihood.  This is the same assumption as used for
+    frequentist hypothesis testing (which is of course equivalent to
+    the asymptotic approximation of a Bayesian posterior with improper
+    uniform priors).  See \hlink{King, Tomz, and Wittenberg
+      (2000)}{http://gking.harvard.edu/files/abs/making-abs.shtml}.
+    For \emph{Bayesian models}, Zelig simulates quantities of interest
+    from the posterior density, whenever possible.  For \emph{robust
+      Bayesian models}, simulations are drawn from the identified
+    class of Bayesian posteriors.
+  
+  \item Alternatively, you may set {\tt bootstrap = TRUE} to simulate
+    parameters using bootstrapped data sets.  If your dataset is
+    large, bootstrap procedures will usually be more memory intensive
+    and time-consuming than simulation using asymptotic normal
+    approximation.  The type of bootstrapping (including the sampling
+    method) is determined by the optional argument {\tt bootfn},
+    described below.
+  
+  \item If {\tt bootstrap = TRUE} is selected, {\tt sim()} will
+    bootstrap parameters using the default {\tt bootfn}, which
+    re-samples from the data frame with replacement to create a
+    sampled data frame of the same number of observations, and then
+    re-runs {\tt zelig()} (inside {\tt sim()}) to create one set of
+    bootstrapped parameters.  Alternatively, you may create a function
+    outside the {\tt sim()} procedure to handle different bootstrap
+    procedures.  Please consult {\tt help(boot)} for more
+    details.\footnote{If you choose to create your own {\tt bootfn},
+      it must include the the following three arguments: {\tt data},
+      the original data frame; one of the sampling methods described
+      in {\tt help(boot)}; and {\tt object}, the original {\tt
+        zelig()} output object.  The alternative bootstrapping
+      function must sample the data, fit the model, and extract the
+      model-specific parameters.}
+  \end{enumerate}
+  
+  For \textbf{conditional} prediction, {\tt sim()} takes only two
+  required arguments:
+  \begin{enumerate}
+  \item {\tt z.out}, the model output from {\tt zelig()}.
+  \item {\tt x}, the conditional output from {\tt setx()}.  
+  \item Optionally, for duration models, {\tt cond.data}, which is the
+    {\tt data} argument from {\tt setx()}.  For models for duration
+    dependent variables (see \Sref{duration}), {\tt sim()} must impute
+    the uncensored dependent variables before calculating the average
+    treatment effect.  Inputting the {\tt cond.data} allows {\tt
+      sim()} to generate appropriate values.
+  \end{enumerate}
+  Additional arguments are ignored or generate error messages.  
+
+\end{enumerate}
+
+\subsubsection*{Presenting Results}
+\begin{enumerate}
+\item Use {\tt summary(s.out)} to print a summary of your simulated
+  quantities.  You may specify the number of significant digits as:
+\begin{verbatim}
+> print(summary(s.out), digits = 2)   
+\end{verbatim}
+\item Alternatively, you can plot your results using
+  \texttt{plot(s.out)}.
+\item You can also use \texttt{names(s.out)} to see the names and a
+  description of the elements in this object and the {\tt \$} operator
+  to extract particular results.  For most models, these are: {\tt
+    s.out\$qi\$pr} (for predicted values), {\tt s.out\$qi\$ev} (for
+  expected values), and {\tt s.out\$qi\$fd} (for first differences in
+  expected values).  For the logit, probit, multinomial logit, ordinal
+  logit, and ordinal probit models, quantities of interest also
+  include {\tt s.out\$qi\$rr} (the risk ratio).
+\end{enumerate} 
+
+\section{Supported Models}\label{s:models}
+
+We list here all models impelmented in Zelig, organized by the nature
+of the dependent variable(s) to be predicted, explained, or described.
+
+\begin{enumerate}
+\item {\bf Continuous Unbounded} dependent variables can take any
+real value in the range $(-\infty, \infty)$. While most of these
+models take a continuous dependent variable, Bayesian factor analysis takes multiple
+continuous dependent variables.  
+
+   \begin{enumerate}
+   \item {\tt "ls"}: The {\it linear least-squares} (see \Sref{ls})
+     calculates the coefficients that minimize the sum of squared
+     residuals.  This is the usual method of computing linear
+     regression coefficients, and returns unbiased estimates of
+     $\beta$ and $\sigma^2$ (conditional on the specified model).
+   \item {\tt "normal"}: The {\it Normal} (see \Sref{normal}) model
+     computes the maximum-likelihood estimator for a Normal
+     stochastic component and linear systematic component.  The
+     coefficients are identical to \texttt{ls}, but the
+     maximum likelihood estimator for $\sigma^2$ is consistent
+     but biased.
+   \item {\tt "normal.bayes"}:  The {\it Bayesian Normal} regression
+model (\Sref{normal.bayes}) is similar to maximum likelihood Gaussian
+regression, but makes valid small sample inferences via draws from the
+exact posterior and also allows for priors.  
+
+
+  \item {\tt "tobit"}:  The {\it tobit} regression model (see 
+  \Sref{tobit}) is a Normal distribution with left-censored observations.
+
+  \item {\tt "tobit.bayes"}: The {\it Bayesian tobit} distribution
+  (see \Sref{tobit.bayes}) is a Normal distribution that has either
+  left and/or right censored observations.  
+
+   \item {\tt "factor.bayes"}: The {\it Bayesian factor analysis} model (see
+\Sref{factor.bayes}) estimates multiple observed continuous dependent variables
+as a function of latent explanatory variables.  
+
+   \end{enumerate}
+ \item {\bf Dichotomous} dependent variables consist of two discrete
+values, usually $(0,1)$.  
+   \begin{enumerate}
+   \item {\tt "logit"}: {\it Logistic regression} (see \Sref{logit})
+     specifies $\Pr(Y=1)$ to be a(n inverse) logistic transformation
+     of a linear function of a set of explanatory variables.
+   \item {\tt "relogit"}: The {\it rare events logistic} regression
+     option (see \Sref{relogit}) estimates the same model as the
+     logit, but corrects for bias due to rare events (when one of the
+     outcomes is much more prevalent than the other).  It also
+     optionally uses prior correction to correct for choice-based
+     (case-control) sampling designs.
+   \item {\tt "logit.bayes"}: {\it Bayesian logistic regression} (see
+\Sref{logit.bayes}) is similar to maximum likelihood logistic
+regression, but makes valid small sample inferences via draws from the
+exact posterior and also allows for priors.  
+   \item {\tt "probit"}: {\it Probit regression} (see \Sref{probit})
+     Specifies $\Pr(Y=1)$ to be a(n inverse) CDF normal transformation
+     as a linear function of a set of explanatory variables.
+   \item {\tt "probit.bayes"}: {\it Bayesian probit} regression (see
+\Sref{probit.bayes}) is similar to maximum likelihood probit
+regression, but makes valid small sample inferences via draws from the
+exact posterior and also allows for priors. 
+   \item {\tt "blogit"}: The {\it bivariate logistic} model (see
+     \Sref{blogit}) models $\Pr(Y_{i1} = y_1, Y_{i2} = y_2)$ for
+$(y_1, y_2) = {(0,0), (0,1), (1,0), (1,1)}$ according 
+to a bivariate logistic density.
+   \item {\tt "bprobit"}: The {\it bivariate probit} model (see
+     \Sref{bprobit}) models $\Pr(Y_{i1} = y_1, Y_{i2} = y_2)$ for
+$(y_1, y_2) = {(0,0), (0,1), (1,0), (1,1)}$ according to a bivariate normal density.  
+   \item {\tt "irt1d"}: The {\it one-dimensional item response} model
+(see \Sref{irt1d}) takes multiple dichotomous dependent variables and models
+them as a function of \emph{one} latent (unobserved) explanatory variable.  
+   \item {\tt "irtkd"}:  The {\it k-dimensional item response} model
+(see \Sref{irtkd}) takes multiple dichotomous dependent variables and models
+them as a function of $k$ latent (unobserved) explanatory variables.  
+\end{enumerate} 
+ \item {\bf  Ordinal} are used to model
+   ordered, discrete dependent variables.  The values of the outcome
+   variables (such as kill, punch, tap, bump) are ordered, but the
+   distance between any two successive categories is not known
+   exactly.  Each dependent variable may be thought of as linear, with
+one continuous, unobserved dependent variable observed through a mechanism
+   that only returns the ordinal choice.
+   \begin{enumerate}
+   \item {\tt "ologit"}: The {\it ordinal logistic} model (see
+     \Sref{ologit}) specifies the stochastic component of the
+     unobserved variable to be a standard logistic distribution.
+   \item {\tt "oprobit"}: The {\it ordinal probit} distribution (see
+     \Sref{oprobit}) specifies the stochastic component of the
+     unobserved variable to be standardized normal.
+      \item {\tt "oprobit.bayes"}: {\it Bayesian ordinal probit} model
+(see \Sref{oprobit.bayes}) is similar to ordinal probit
+regression, but makes valid small sample inferences via draws from the
+exact posterior and also allows for priors.  
+     \item {\tt "factor.ord"}: {\it Bayesian ordered factor analysis}
+(see \Sref{factor.ord}) models observed, ordinal dependent variables
+as a function of latent explanatory variables.  
+   \end{enumerate}
+ \item {\bf Multinomial} dependent variables are unordered, discrete
+   categorical responses.  For example, you could model an
+   individual's choice among brands of orange juice or among
+   candidates in an election.
+    \begin{enumerate}
+    \item {\tt "mlogit"}: The {\it multinomial logistic} model (see
+      \Sref{mlogit}) specifies categorical responses distributed
+      according to the multinomial stochastic component and logistic
+      systematic component.
+    \item {\tt "mlogit.bayes"}: {\it Bayesian multinomial logistic} regression (see
+\Sref{mlogit.bayes}) is similar to maximum likelihood multinomial logistic
+regression, but makes valid small sample inferences via draws from the
+exact posterior and also allows for priors. 
+% \item {\tt "mprobit"}: The {\it multinomial probit} model (see
+%      \Sref{mprobit}) specifies categorical responses distributed
+%      according to the multinomial stochastic component and Normal
+%      systematic component.
+    \end{enumerate}
+  \item {\bf Count} dependent variables are non-negative integer
+values, such as the number of presidential vetoes or the number of
+photons that hit a detector. 
+    \begin{enumerate}
+    \item {\tt "poisson"}: The {\it Poisson} model (see
+      \Sref{poisson}) specifies the expected number of events that
+      occur in a given observation period to be an exponential
+      function of the explanatory variables.  The Poisson stochastic
+      component has the property that, $\lambda = \textrm{E}(Y_i|X_i)
+      = \textrm{V}(Y_i|X_i)$.
+    \item {\tt "poisson.bayes"}: {\it Bayesian Poisson} regression (see
+\Sref{poisson.bayes}) is similar to maximum likelihood Poisson
+regression, but makes valid small sample inferences via draws from the
+exact posterior and also allows for priors. 
+    \item {\tt "negbin"}: The {\it negative binomial} model (see
+      \Sref{negbin}) has the same systematic component as the Poisson,
+      but allows event counts to be over-dispersed, such that
+      $\textrm{V}(Y_i|X_i) > \textrm{E}(Y_i|X_i)$.
+    \end{enumerate}
+
+  \item {\bf Continuous Bounded}\label{duration} dependent variables
+that are continuous only over a certain range, usually $(0, \infty)$.
+In addition, some models (exponential, lognormal, and
+Weibull) are also censored for values greater than some censoring
+point, such that the dependent variable has some units fully observed
+and others that are only partially observed (censored).  
+
+ \begin{enumerate}
+
+   \item {\tt "gamma"}: The {\it Gamma} model (see \Sref{gamma}) for
+     positively-valued, continuous dependent variables that are fully
+observed (no censoring).  
+
+   \item {\tt "exp"}: The {\it exponential} model (see \Sref{exp}) for
+   right-censored dependent variables assumes that the hazard function
+   is constant over time.  For some variables, this may be an unrealistic
+   assumption as subjects are more or less likely to fail the longer
+   they have been exposed to the explanatory variables.
+
+  \item {\tt "weibull"}: The {\it Weibull} model (see \Sref{weibull})
+  for right-censored dependent variables relaxes the
+  assumption of constant hazard by including an additional scale
+  parameter $\alpha$: If $\alpha > 1$, the risk of failure increases
+  the longer the subject has survived; if $\alpha < 1$, the risk of
+  failure decreases the longer the subject has survived.  While {\tt
+  zelig()} estimates $\alpha$ by default, you may optionally fix
+  $\alpha$ at any value greater than 0.  Fixing $\alpha = 1$ results
+  in an exponential model.
+
+  \item {\tt "lognorm"}: The {\it log-normal} model (see
+  \Sref{lognorm}) for right-censored duration dependent variables
+  specifies the hazard function non-monotonically, with increasing
+  hazard over part of the observation period and decreasing hazard
+  over another.
+
+\end{enumerate}
+
+\item {\bf Mixed} dependent variables include models that take more
+than one dependent variable, where the dependent variables come from
+two or more of categories above.  (They do not need
+to be of a homogeneous type.)
+\begin{enumerate}
+\item The {\it Bayesian mixed factor analysis} model, in contrast to the Bayesian factor
+analysis model and ordinal factor analysis model, can model both types
+of dependent variables as a function of latent explanatory variables.
+\end{enumerate}
+
+\item {\bf Ecological inference} models estimate unobserved internal
+cell values given contingency tables with observed row and column
+marginals.  
+\begin{enumerate}
+\item {\tt ei.hier}: The {\it hierarchical {\sc ei}} model
+(see \Sref{ei.hier}) produces estimates for a cross-section of $2 \times 2$
+tables.   
+\item {\tt ei.dynamic}: {\it Quinn's dyanamic Bayesian {\sc ei}} model (see
+\Sref{ei.dynamic}) estimates a dynamic Bayesian model for $2 \times 2$
+tables with temporal dependece across tables.
+\item {\tt ei.RxC}: The $R \times C$ {\sc ei} model (see \Sref{eiRxC})
+estimates a hierarchical Multinomial-Dirichlet {\sc ei} model for
+contingency tables with more than 2 rows or columns.  
+\end{enumerate}
+
+
+%  \item {\bf Models for Contingency Tables}
+%     \begin{enumerate}
+%     \item {\tt "mloglm"}: Use a {\it multinomial log-linear} model (see
+%       \Sref{mloglm}) to estimate log-linear models for contingency
+%       tables.  These models use raw counts in tables as the dependent
+%       variable and apply a Poisson regression model.  Many of the rich
+%       set of models that result are asymptotically equavlent to
+%       multinomial logit models.  
+%     \end{enumerate}
+\end{enumerate}
+
+\section{Replication Procedures} 
+
+A large part of any statistical analysis is documenting your work such
+that given the same data, anyone may replicate your results.  In
+addition, many journals require the creation and dissemination of
+``replication data sets'' in order that others may replicate your
+results (see \hlink{King,
+1995}{http://gking.harvard.edu/data.shtml\#repl}\nocite{King95}).
+Whether you wish to create replication materials for your own records,
+or contribute data to others as a companion to your published work,
+Zelig makes this process easy.
+
+\subsection{Saving Replication Materials}
+
+Let {\tt mydata} be your final data set, {\tt z.out} be your {\tt
+  zelig()} output, and {\tt s.out} your {\tt sim()} output.  To save
+all of this in one file, type:
+\begin{verbatim}
+> save(mydata, z.out, s.out, file = "replication.RData")
+\end{verbatim}
+This creates the file replication.RData in your working directory.
+You may compress this file using zip or gzip tools.
+
+If you have run several specifications, all of these estimates may be
+saved in one .RData file.  Even if you only created quantities of
+interest from one of these models, you may still save all the
+specifications in one file.  For example:
+\begin{verbatim}
+> save(mydata, z.out1, z.out2, s.out, file = "replication.RData")
+\end{verbatim}
+
+Although the .RData format can contain data sets as well as output
+objects, it is not the most space-efficient way of saving large data
+sets.  In an uncompressed format, ASCII text files take up less space
+than data in .RData format.  (When compressed, text-formatted data is
+still smaller than .RData-formatted data.)  Thus, if you have more
+than 100,000 observations, you may wish to save the data set
+separately from the Zelig output objects.  To do this, use the {\tt
+  write.table()} command.  For example, if {\tt mydata} is a data
+frame in your workspace, use {\tt write.table(mydata, file =
+  "mydata.tab")} to save this as a tab-delimited ASCII text file.  You
+may specify other delimiters as well; see {\tt
+  help.zelig("write.table")} for options.
+
+\subsection{Replicating Analyses}
+
+If the data set and analyses are all saved in one .RData file, located
+in your working directory, you may simply type:
+\begin{verbatim}
+> load("replication.RData")                   # Loads the replication file.  
+> z.rep <- repl(z.out)                        # To replicate the model only. 
+> s.rep <- repl(s.out)                        # To replicate the model and 
+                                              #  quantities of interest.  
+\end{verbatim}
+By default, {\tt repl()} uses the same options used to create the
+original output object.  Thus, if the original {\tt s.out} object used
+bootstrapping with 245 simulations, the {\tt s.rep} object will
+similarly have 245 bootstrapped simulations.  In addition, you may use
+the {\tt prev} option when replicating quantities of interest to reuse
+rather than recreate simulated parameters.  Type {\tt
+  help.zelig("repl")} to view the complete list of options for
+{\tt repl()}.
+
+If the data were saved in a text file, use {\tt read.table()} to load
+the data, and then replicate the analysis:
+\begin{verbatim}
+> dat <- read.table("mydata.tab", header = TRUE)  # Where `dat' is the same
+> load("replication.RData")                       #   as the name used in 
+> z.rep <- repl(z.out)                            #   `z.out'.
+> s.rep <- repl(s.out)  
+\end{verbatim}
+If you have problems loading the data, please refer to \Sref{load.data}.
+
+Finally, you may use the {\tt identical()} command to ensure that the
+replicated regression output is in every way identical to the original
+{\tt zelig()} output.\footnote{The {\tt identical()} command checks
+  that numeric values are identical to the maximum number of decimal
+  places (usually 16), and also checks that the the two objects have
+  the same class (numeric, character, integer, logical, or factor).
+  Refer to {\tt help(identical)} for more information.}  For example:
+\begin{verbatim}
+> identical(z.out$coef, z.rep$coef)              # Checks the coefficients.
+\end{verbatim}
+Simulated quantities of interest will vary from the original
+quantities if parameters are re-simulated or re-sampled.  If you wish to
+use {\tt identical()} to verify that the quantities of interest are
+identical, you may use
+\begin{verbatim}
+# Re-use the parameters simulated (and stored) in the original sim() output.
+> s.rep <- repl(s.out, prev = s.out$par) 
+
+# Check that the expected values are identical.  You may do this for each qi.
+> identical(s.out$qi$ev, s.rep$qi$ev) 
+\end{verbatim} %$
+
+
diff --git a/inst/doc/acknowledgments.aux b/inst/doc/acknowledgments.aux
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diff --git a/inst/doc/acknowledgments.tex b/inst/doc/acknowledgments.tex
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+\section*{Acknowledgments}  
+
+The Zelig project would not have been possible without considerable
+help from many sources.  Our special thanks go to the \hlink{R core
+  team}{http://www.r-project.org/contributors.html} for providing an
+excellent modular, open-source platform for the entire statistics and
+methodological community.
+
+The authors of the following R packages have provided some of the
+models available through Zelig: {\tt MASS} by William N. Venables and
+Brian D. Ripley; {\tt MCMCpack} by Andrew D. Martin and Kevin M.
+Quinn; {\tt survival} by Terry Therneau and Brian D. Ripley; and {\tt
+  VGAM} by Thomas Yee.  
+
+The authors of the following R packages have provided some of the
+auxiliary statistical procedures and methods available through Zelig:
+{\tt boot} by Angelo Canty and Brian Ripley; {\tt coda} by Martyn
+Plummer, Nicky Best, Kate Cowles, and Karen Vines; {\tt sandwich} by
+Achim Zeileis; and {\tt zoo} by Achim Zeileis and Gabor Grothendieck.
+
+Our appreciation also goes to Ferdinand Almahdi, Ben Goodrich, Justin 
+Grimmer, Yang Lin, and Ying Lu, who have contributed either code or 
+documentation; Dan Hopkins, Ian Yohai, and others who have given valuable 
+feedback from the courses in which they have used Zelig as a teaching 
+tool.
+
+For research support, we thank the National Institutes of Aging (P01
+AG17625-01), the National Science Foundation (SES-0318275,
+SES-0550873, IIS-9874747, SES-0112072), the Mexican Ministry of
+Health, the U.S.\ Library of Congress (PA\# NDP03-1), and the
+Princeton University Committee on Research in the Humanities and
+Social Sciences.
+
+As usual, all errors are our responsibility.
+
+
diff --git a/inst/doc/addingModels.tex b/inst/doc/addingModels.tex
new file mode 100644
index 0000000..523fcd1
--- /dev/null
+++ b/inst/doc/addingModels.tex
@@ -0,0 +1,496 @@
+\chapter{Adding Models and Methods to Zelig}
+\label{c:addingmodels}
+
+Zelig is highly modular.  You can add methods to Zelig \emph{and}, if
+you wish, release your programs as a stand-alone package.  By making
+your package compatible with Zelig, you will advertise your package
+and help it achieve a widespread distribution.  
+
+This chapter assumes that your model is written as a function that
+takes a user-defined formula and data set (see Chapter \ref{s:new}), and returns
+a list of output that includes (at the very least) the estimated
+parameters and terms that describe the data used to
+fit the model.  You should choose a class for this list of output, and
+provide appropriate methods for generic functions such as {\tt
+summary()}, {\tt print()}, {\tt coef()} and {\tt vcov()}.  
+
+To add new models to Zelig, you need to provide six R functions,
+illustrated in Figure \ref{add}.  Let {\tt mymodel} be a
+new model with class {\tt "myclass"}.
+
+\begin{figure*}[h!]
+\caption{Six functions (solid boxes) to implement a new Zelig model}
+\label{add}
+\begin{center}
+\setlength{\unitlength}{0.5mm}
+\begin{picture}(160,170)(0,0)
+\linethickness{0.75pt}
+
+\put(0,166){Estimate}
+
+\put(70,162){\line(0,-1){42}}
+\put(50,162){\dashbox{2}(40,12){{\tt zelig()}}}
+\multiput(70,144)(0,-24){2}{\line(1,0){9}}
+\put(80,138){\framebox(83,12){(1) {\tt zelig2mymodel()}}}
+\put(80,114){\framebox(57,12){(2) {\tt mymodel()}}}
+
+\put(0,96){Interpret}
+
+\put(70,92){\line(0,-1){42}}
+\put(50,92){\dashbox{2}(40,12){{\tt sim()}}}
+\multiput(70,74)(0,-24){2}{\line(1,0){9}}
+\put(80,68){\framebox(83,12){(3) {\tt param.myclass()}}}
+\put(80,44){\framebox(69,12){(4) {\tt qi.myclass()}}}
+
+\put(0,26){Plot}
+
+\put(50,0){\framebox(105,12){(6) {\tt plot.zelig.mymodel()}}}
+
+\end{picture}
+\end{center}
+\end{figure*}
+
+These functions are as follows:  
+\begin{enumerate}
+\item {\tt zelig2mymodel()} translates {\tt zelig()} arguments into
+the arguments for {\tt mymodel()}.
+\item {\tt mymodel()} estimates your statistical procedure.
+\item {\tt param.myclass()} simulates parameters for your model.
+Alternatively, if your model's parameters consist of one vector with a
+correspondingly observed variance-covariance matrix, you may write
+\emph{two} simple functions to substitute for {\tt param.myclass()}:  
+\begin{enumerate}
+\item {\tt coef.myclass()} to extract the coefficients from your model
+output, and
+\item {\tt vcov.myclass()} to extract the variance-covariance matrix
+from your model.  
+\end{enumerate}
+\item {\tt qi.myclass()} calculates expected values, simulates
+predicted values, and generates other quantities of interest for your
+model (applicable only to models that take explanatory variables).  
+\item {\tt plot.zelig.mymodel()} to plot the simulated quantities of
+interest from your model.  
+\item A {\bf reference manual page} to document the model.
+  (See~\Sref{s:format})
+\item A function ({\tt describe.mymodel()}) describing the inputs to
+your model, for use with a graphical user interface.  (See \Sref{describe.mymodel}).  
+\item An optional {\bf demo script} {\tt mymodel.R} which contains commented code for
+  the models contained in the example section of your reference manual
+  page.
+\end{enumerate}
+
+\section{Making the Model Compatible with Zelig}\label{compatible}
+
+You can develop a model, write the model-fitting function, and test it
+within the Zelig framework without explicit intervention from the
+Zelig team.  (We are, of course, happy to respond to any questions or
+suggestions for improvement.)  
+
+Zelig's modularity relies on two R programming conventions:
+\begin{enumerate}
+\item \emph{\bf wrappers}, which pass arguments from R functions to
+  other R functions or to foreign function calls (such as C, C++, or
+  Fortran functions); and 
+\item \emph{\bf classes}, which tell generic functions how to handle
+  objects of a given class.  
+\end{enumerate}
+Specific methods for R generic functions take the general form: {\tt
+  method.class()}, where {\tt method} is the name of the generic
+procedure to be performed and {\tt class} is the class of the object.
+You may define, for example, {\tt summary.contrib()} to summarize the
+output of your model.
+
+\subsubsection{To Work with {\tt zelig()}}
+
+Zelig has implemented a unique method for incorporating new models
+which lets contributors test their models \emph{within} the Zelig
+framework \emph{without} any modification of the {\tt zelig()}
+function itself.  
+
+Using a wrapper function {\tt zelig2contrib()} (where {\tt contrib} is
+the name of your new model), {\tt zelig2contrib()} redefines the inputs to
+{\tt zelig()} to work with the inputs you need for your function {\tt
+contrib()}.  For example, if you type
+\begin{verbatim}
+zelig(..., model = "normal.regression")
+\end{verbatim}
+{\tt zelig()} looks for a {\tt zelig2normal.regression()} wrapper in any
+environment (either attached libraries or your workspace).  If the
+wrapper exists, then {\tt zelig()} runs the model.  
+
+If you have a pre-existing model, writing a {\tt zelig2contrib()}
+function is quite easy.  Let's say that your model is {\tt contrib()},
+and takes the following arguments: {\tt formula}, {\tt data}, {\tt
+  weights}, and {\tt start}.  The {\tt zelig()} function, in contrast,
+only takes the {\tt formula}, {\tt data}, {\tt model}, and {\tt by}
+arguments.  You may use the {\tt \dots} to pass additional arguments
+from {\tt zelig()} to {\tt zelig2contrib()}, and {\tt <- NULL} to omit
+the elements you do not need.  Continuing the Normal regression example
+from~\Sref{normal.regression}, let {\tt formula}, {\tt model}, and {\tt data} be
+the inputs to {\tt zelig()}, {\tt M} is the number of subsets, and
+{\tt \dots} are the additional arguments not defined in the {\tt
+  zelig()} call, but passed to {\tt normal.regression()}.
+\begin{verbatim}
+zelig2normal.regression <- function(formula, model, data, M, ...) {
+  mf <- match.call(expand.dots = TRUE)                     # [1]
+  mf$model <- mf$M <- NULL                                 # [2]
+  mf[[1]] <- as.name("normal.regression")                  # [3]
+  as.call(mf)                                              # [4] 
+}
+\end{verbatim}
+The bracketed numbers above correspond to the comments below: 
+\begin{enumerate}
+\item Create a call (an expression to be evaluated) by
+  creating a list of the arguments in {\tt zelig2normal.regression()}, including
+  the extra arguments taken by {\tt normal.regression()}, but not by {\tt zelig()}.
+  All wrappers must take the same standardized arguments ({\tt
+    formula}, {\tt model}, {\tt data}, and {\tt M}), which may be used
+  in the wrapper function to manipulate the {\tt zelig()} call into
+  the {\tt normal.regression()} call.  Additional arguments to {\tt normal.regression()}, such
+  as {\tt start.val} are passed implicitly from {\tt zelig()} using the
+  {\tt ...} operator.
+  
+  \item Erase extraneous information from the call
+    object {\tt mf}.  In this wrapper, {\tt model} and {\tt M} are not
+    used.  In other models, these are used to further manipulate the
+    call, and so are included in the standard inputs to all wrappers.
+
+\item  Reassign the first element of the call
+  (currently {\tt zelig2normal.regression}) with the name of the function to be
+  evaluated, {\tt normal.regression()}.  
+
+\item Return the call to {\tt zelig()}, which will
+  evaluate the call for each multiply-imputed data set, each subset
+  defined in {\tt by}, or simply {\tt data}.  
+\end{enumerate}
+
+\subsubsection{To Work with {\tt setx()}}
+
+In the case of {\tt setx()}, most models will use {\tt
+  setx.default()}, which in turn relies on the generic R function {\tt
+  model.matrix()}.  For this procedure to work, your list of output
+must include:  
+\begin{itemize}
+\item {\tt terms}, created by {\tt model.frame()}, or manually;
+\item {\tt formula}, the formula object input by the user;
+\item {\tt xlevels}, which define the strata in the explanatory
+  variables; and
+\item {\tt contrasts}, an optional element which defines the type of
+  factor variables used in the explanatory variables.  See {\tt
+    help(contrasts)} for more information.
+\end{itemize}
+
+If your model output does not work with {\tt setx.default()}, you must
+write your own {\tt setx.contrib()} function.  For example, models fit
+to multiply-imputed data sets have output from {\tt zelig()} of class
+{\tt "MI"}.  The special {\tt setx.MI()} wrapper pre-processes the
+{\tt zelig()} output object and passes the appropriate arguments to
+{\tt setx.default()}.  
+
+\subsubsection{Compatibility with {\tt sim()}}
+
+Simulating quantities of interest is an integral part of interpreting
+model results.  To use the functionality built into the Zelig {\tt
+sim()} procedure, you need to provide a way to simulate parameters
+(called a {\tt param()} function), and a method for calculating or
+drawing quantities of interest from the simulated parameters (called a
+{\tt qi()} function).
+
+\paragraph{Simulating Parameters}
+
+Whether you choose to use the default method, or write a
+model-specific method for simulating parameters, these functions
+require the same three inputs:  
+\begin{itemize}
+\item {\tt object}: the estimated model or {\tt zelig()} output.
+\item {\tt num}: the number of simulations.
+\item {\tt bootstrap}: either {\tt TRUE} or {\tt FALSE}.  
+\end{itemize}
+The output from {\tt param()} should be either
+\begin{itemize}
+\item If {\tt bootstrap = FALSE} (default), an matrix with rows
+corresponding to simulations and columns corresponding to model
+parameters.  Any ancillary parameters should be included in the output
+matrix.  
+\item If {\tt bootstrap = TRUE}, a vector containing all model
+parameters, including ancillary parameters.    
+\end{itemize} 
+
+There are two ways to simulate parameters:
+\begin{enumerate}
+  
+\item Use the {\tt param.default()} function to extract parameters
+  from the model and, if bootstrapping is not selected, simulate
+  coefficients using asymptotic normal approximation.  The {\tt
+    param.default()} function relies on two R functions:
+
+  \begin{enumerate}
+  \item {\tt coef()}: extracts the coefficients.  Continuing the
+    Normal regression example from above, the appropriate {\tt coef.normal()}
+    function is simply:
+\begin{verbatim}
+coef.normal <- function(object)
+  object$coefficients
+\end{verbatim} %$
+  \item {\tt vcov()}: extracts the variance-covariance matrix.  Again
+    continuing the Poisson example from above:
+\begin{verbatim}
+vcov.normal <- function(object)
+  object$variance
+\end{verbatim} %$
+\end{enumerate}
+
+\item Alternatively, you can write your own {\tt param.contrib()}
+  function.  This is appropriate when:  
+  \begin{enumerate}
+  \item Your model has auxiliary parameters, such as $\sigma$ in the
+    case of the Normal distribution.
+  \item Your model performs some sort of correction to the coefficients
+    or the variance-covariance matrix, which cannot be performed in
+    either the {\tt coef.contrib()} or the {\tt vcov.contrib()}
+    functions.
+  \item Your model does not rely on asymptotic approximation to the
+    log-likelihood.  For Bayesian Markov-chain monte carlo models, for
+    example, the {\tt param.contrib()} function ({\tt
+      param.MCMCzelig()} in this case) simply extracts the model
+    parameters simulated in the model-fitting function.
+  \end{enumerate}
+  Continuing the Normal example, 
+\begin{verbatim}
+param.normal <- function(object, num = NULL, bootstrap = FALSE, 
+                   terms = NULL) {
+  if (!bootstrap) {
+    par <- mvrnorm(num, mu = coef(object), Sigma = vcov(object))
+    Beta <- parse.par(par, terms = terms, eqn = "mu")
+    sigma2 <- exp(parse.par(par, terms = terms, eqn = "sigma2"))
+    res <- cbind(Beta, sigma2)
+  }
+  else {
+    par <- coef(object)
+    Beta <- parse.par(par, terms = terms,  eqn = "mu")
+    sigma2 <- exp(parse.par(par, terms = terms, eqn = "sigma2"))
+    res <- c(coef, sigma2)
+  }
+  res
+}
+\end{verbatim}
+\end{enumerate}
+
+\paragraph{Calculating Quantities of Interest}
+
+All models require a model-specific method for calculating quantities
+of interest from the simulated parameters.  For a model of class {\tt
+  contrib}, the appropriate {\tt qi()} function is {\tt
+  qi.contrib()}.  This function should calculate, at the bare minimum,
+the following quantities of interest:
+
+\begin{itemize}
+\item {\tt ev}:  the expected values, calculated from the analytic
+  solution for the expected value as a function of the systematic
+  component and ancillary parameters.  
+\item {\tt pr}:  the predicted values, drawn from a distribution
+  defined by the predicted values.  If R does not have a built-in
+  random generator for your function, you may take a random draw from
+  the uniform distribution and use the inverse CDF method to calculate
+  predicted values.  
+\item {\tt fd}: first differences in the expected value, calculated
+  by subtracting the expected values given the specified {\tt x} from
+  the expected values given {\tt x1}.   
+\item {\tt ate.ev}: the average treatment effect calculated using the
+  expected values {\tt ev}.  This is simply {\tt y - ev}, averaged across
+  simulations for each observation.  
+\item {\tt ate.pr}:  the average treatment effect calculated using
+  the predicted values {\tt pr}.  This is simply {\tt y - pr},
+  averaged across simulations for each observation.   
+\end{itemize}
+The required arguments for the {\tt qi()} function are:  
+\begin{itemize}
+\item {\tt object}: the zelig output object.
+\item {\tt par}: the simulated parameters. 
+\item {\tt x}: the matrix of explanatory variables (created using {\tt
+    setx()}).
+\item {\tt x1}: the optional matrix of alternative values for first
+  differences (also created using {\tt setx()}).  If first differences
+  are inappropriate for your model, you should put in a {\tt
+    warning()} or {\tt stop()} if {\tt x1} is not {\tt NULL}.  
+\item {\tt y}: the optional vector or matrix of dependent variables
+  (for calculating average treatment effects).  If average treatment
+  effects are inappropriate for your model, you should put in a {\tt
+    warning()} or {\tt stop()} if conditional prediction has been
+  selected in the {\tt setx()} step.  
+\end{itemize}
+
+Continuing the Normal regression example from above, the appropriate {\tt
+  qi.normal()} function is as follows:  
+\begin{verbatim}
+qi.normal <- function(object, par, x, x1 = NULL, y = NULL) {
+  Beta <- parse.par(par, eqn = "mu")                        # [1]
+  sigma2 <- parse.par(par, eqn = "sigma2")                  # [2]
+  ev <- Beta %*% t(x)                                       # [3a]
+  pr <- matrix(NA, ncol = ncol(ev), nrow = nrow(ev))
+  for (i in 1:ncol(ev))                                           
+    pr[,i] <- rnorm(length(ev[,i]), mean = ev[,i],          # [4]
+                    sigma = sd(sigma2[i]))
+  qi <- list(ev = ev, pr = pr)
+  qi.name <- list(ev = "Expected Values: E(Y|X)",
+                  pr = "Predicted Values: Y|X")
+  if (!is.null(x1)){
+    ev1 <- par %*% t(x1)                                    # [3b]
+    qi$fd <- ev1 - ev
+    qi.name$fd <- "First Differences in Expected Values: E(Y|X1)-E(Y|X)"
+  }
+  if (!is.null(y)) {
+    yvar <- matrix(rep(y, nrow(par)), nrow = nrow(par), byrow = TRUE)
+    tmp.ev <- yvar - qi$ev
+    tmp.pr <- yvar - qi$pr
+    qi$ate.ev <- matrix(apply(tmp.ev, 1, mean), nrow = nrow(par))
+    qi$ate.pr <- matrix(apply(tmp.pr, 1, mean), nrow = nrow(par))
+    qi.name$ate.ev <- "Average Treatment Effect: Y - EV"
+    qi.name$ate.pr <- "Average Treatment Effect: Y - PR"
+  }
+  list(qi=qi, qi.name=qi.name)
+}
+\end{verbatim}
+There are five lines of code commented above.  By changing these five
+lines in the following \emph{four} ways, you can write {\tt qi()}
+function appropriate to almost any model:  
+\begin{enumerate}  
+\item Extract any systematic parameters by substituting the name of
+your systematic parameter (defined in {\tt describe.mymodel()}).   
+\item Extract any ancillary parameters (defined in {\tt
+describe.mymodel()}) by substituting their names
+here.  
+\item Calculate the expected value using the inverse link function and
+$\eta = X \beta$.  (For the normal model, this is linear.)  You will
+need to make this change in two places, at Comment [3a] and [3b].  
+\item Replace {\tt rnorm()} with a function that takes random draws
+from the stochastic component of your model.  
+\end{enumerate}
+
+\section{Getting Ready for the GUI}  
+
+Zelig can work with a variety of graphical user interfaces (GUIs).  GUIs
+work by knowing {\it a priori} what a particular model
+accepts, and presenting only those options to the user in some sort of
+graphical interface.  Thus, in order for your model to work with a
+GUI, you must describe your model in terms that the GUI can
+understand.  For models written using the guidelines in Chapter
+\ref{s:new}, your model will be compatible with (at least)
+the \hlink{Virtual Data Center}{http://thedata.org} GUI.  For
+pre-existing models, you will need to create a {\tt describe.*()}
+function for your model following the examples in
+\Sref{describe.mymodel}.  
+
+\section{Formatting Reference Manual Pages}  \label{s:format}
+
+One of the primary advantages of Zelig is that it fully documents the
+included models, in contrast to the programming-orientation of R
+documentation which is organized by function.  Thus, we ask that Zelig
+contributors provide similar documentation, including the syntax and
+arguments passed to {\tt zelig()}, the systematic and stochastic
+components to the model, the quantities of interest, the output
+values, and further information (including references).  There are
+several ways to provide this information:  
+\begin{itemize}
+\item If you have an existing package documented using the .Rd help
+  format, {\tt help.zelig()} will automatically search R-help in
+  addition to Zelig help.
+\item If you have an existing package documented using on-line HTML
+  files with static URLs (like Zelig or MatchIt), you need to provide
+  a {\tt PACKAGE.url.tab} file which is a two-column table containing
+  the name of the function in the first column and the url in the
+  second.  (Even though the file extension is {\tt .url.tab}, the file
+  should be a tab- or space-delimited text file.)  For example:  
+\begin{verbatim}
+command       http://gking.harvard.edu/zelig/docs/Main_Commands.html
+model         http://gking.harvard.edu/zelig/docs/Specific_Models.html
+\end{verbatim}
+If you wish to test to see if your {\tt .url.tab} files works, simply
+place it in your R library/Zelig/data/ directory.  (You do not need to
+reinstall Zelig to test your {\tt .url.tab} file.)
+\item Preferred method:  You may provide a \LaTeXe\ {\tt .tex} file.  This document uses
+  the book style and supports commands from the following packages:
+  {\tt graphicx}, {\tt natbib}, {\tt amsmath}, {\tt amssymb}, {\tt
+    verbatim}, {\tt epsf}, and {\tt html}.  Because model pages are
+  incorporated into this document using {\tt $\backslash$include\{\}},
+  you should make sure that your document compiles before submitting
+  it.  Please adhere to the following conventions for your model page: 
+  \begin{enumerate}
+  \item All mathematical formula should be typeset using the {\tt
+      equation*} and {\tt array}, {\tt eqnarray*}, or {\tt align}
+    environments.  Please avoid {\tt displaymath}.  (It looks funny in
+    html.)
+  \item All commands or R objects should use the {\tt texttt}
+    environment.
+  \item The model begins as a subsection of a larger document, and
+    sections within the model page are of sub-subsection level.
+\item For stylistic consistency, please avoid using the {\tt
+    description} environment.
+\end{enumerate}
+
+Each \LaTeX\ model page should include the following elements.  Let
+{\tt contrib} specify the new model.
+
+\subsubsection*{Help File Template}
+\begin{verbatim}
+\subsection{{\tt contrib}: Full Name for [type] Dependent Variables}
+\label{contrib}
+
+\subsubsection{Syntax}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item First Example
+\item Second Example
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item The observation mechanism, if applicable.
+\item The stochastic component.
+\item The systematic component.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected value of your distribution, including the formula
+  for the expected value as a function of the systemic component and
+  ancillary paramters.  
+\item The predicted value drawn from the distribution defined by the
+       corresponding expected value.  
+\item The first difference in expected values, given when x1 is specified.  
+\item Other quantities of interest.
+\end{itemize}
+
+\subsubsection{Output Values}
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item 
+   \item 
+   \end{itemize}
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item 
+   \item 
+   \end{itemize}
+\item From the {\tt sim()} output stored in {\tt s.out}:
+   \begin{itemize}
+   \item 
+   \item 
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Further Information}
+
+\subsubsection{Contributors}
+\end{verbatim}
+\end{itemize} 
+
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/commands/zelig"
+%%% End: 
diff --git a/inst/doc/advancedR.tex b/inst/doc/advancedR.tex
new file mode 100644
index 0000000..5f6ddcf
--- /dev/null
+++ b/inst/doc/advancedR.tex
@@ -0,0 +1,560 @@
+\chapter{R Objects}\label{a:R}
+
+In R, objects can have one or more classes, consisting of the class of
+the scalar value and the class of the data structure holding the
+scalar value.  Use the {\tt is()} command to determine what an object
+\emph{is}.  If you are already familiar with R objects, you may skip
+to \Sref{load.data} for loading data, or \Sref{s:commands} for a
+description of Zelig commands.
+
+\section{Scalar Values}\label{variables}
+
+R uses several classes of scalar values, from which it constructs 
+larger data structures.  R is highly class-dependent: certain
+operations will only work on certain types of values or certain types
+of data structures.  We list the three basic types of scalar values
+here for your reference:  
+
+\begin{enumerate}
+\item \textbf{Numeric} is the default value type for most numbers.  An
+  \texttt{integer} is a subset of the \texttt{numeric} class, and may
+  be used as a \texttt{numeric} value.  You can perform
+  any type of math or logical operation on numeric values,
+  including:
+\begin{verbatim}
+> log(3 * 4 * (2 + pi))         # Note that pi is a built-in constant, 
+   [1] 4.122270                 #   and log() the natural log function.
+> 2 > 3                         # Basic logical operations, including >,
+   [1] FALSE                    #   <, >= (greater than or equals), 
+                                #   <= (less than or equals), == (exactly 
+                                #   equals), and != (not equals). 
+> 3 >= 2 && 100 == 1000/10      # Advanced logical operations, including
+   [1] TRUE                     #   & (and), && (if and only if), | (or), 
+                                #   and || (either or).
+\end{verbatim}
+  Note that \texttt{Inf} (infinity), \texttt{-Inf} (negative
+  infinity), \texttt{NA} (missing value), and \texttt{NaN} (not a
+  number) are special numeric values on which most math operations
+  will fail.  (Logical operations will work, however.)
+
+\item \textbf{Logical} operations create logical values of either
+  \texttt{TRUE} or \texttt{FALSE}.  To convert logical values to
+  numerical values, use the \texttt{as.integer()} command:
+\begin{verbatim}
+> as.integer(TRUE)
+   [1] 1 
+> as.integer(FALSE)
+   [1] 0
+\end{verbatim}
+\item \textbf{Character} values are text strings.  For example, 
+\begin{verbatim}
+> text <- "supercalafragilisticxpaladocious"
+> text
+[1] "supercalafragilisticxpaladocious"
+\end{verbatim}
+  assigns the text string on the right-hand side of the \texttt{<-} to
+  the named object in your workspace.  Text strings are primarily used
+  with data frames, described in the next section.  R always returns
+  character strings in quotes.
+\end{enumerate}
+
+\section{Data Structures}
+
+\subsection{Arrays}
+
+Arrays are data structures that consist of only one type of scalar
+value (e.g., a vector of character strings, or a matrix of numeric
+values).  The most common versions, one-dimensional and
+two-dimensional arrays, are known as \emph{vectors} and
+\emph{matrices}, respectively.  
+
+\subsubsection*{Ways to create arrays}
+\begin{enumerate}
+\item Common ways to create {\bf vectors} (or one-dimensional arrays)
+include:  
+\begin{verbatim}
+> a <- c(3, 7, 9, 11)    # Concatenates numeric values into a vector
+> a <- c("a", "b", "c")  # Concatenates character strings into a vector
+> a <- 1:5               # Creates a vector of integers from 1 to 5 inclusive  
+> a <- rep(1, times = 5) # Creates a vector of 5 repeated `1's
+\end{verbatim}
+To manipulate a vector:  
+\begin{verbatim}
+> a[10]                # Extracts the 10th value from the vector `a'
+> a[5] <- 3.14         # Inserts 3.14 as the 5th value in the vector `a'
+> a[5:7] <- c(2, 4, 7) # Replaces the 5th through 7th values with 2, 4, and 7
+\end{verbatim}
+\emph{Unlike} larger arrays, vectors can be extended without first
+creating another vector of the correct length.  Hence, 
+\begin{verbatim}
+> a <- c(4, 6, 8) 
+> a[5] <- 9       # Inserts a 9 in the 5th position of the vector,
+                  #  automatically inserting an `NA' values position 4 
+\end{verbatim}
+
+\item \label{factors} A {\bf factor vector} is a special type of
+vector that allows users to create $j$ indicator variables in one
+vector, rather than using $j$ dummy variables (as in Stata or SPSS).  R
+creates this special class of vector from a pre-existing vector {\tt x}
+using the {\tt factor()} command, which separates {\tt x} into levels
+based on the discrete values observed in {\tt x}.  These values may be
+either integer value or character strings.  For example,
+\begin{verbatim}
+> x <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 9, 9, 9, 9)
+> factor(x)
+   [1] 1 1 1 1 1 2 2 2 2 9 9 9 9
+   Levels: 1 2 9
+\end{verbatim}
+By default, {\tt factor()} creates unordered factors, which are
+  treated as discrete, rather than ordered, levels.  Add the optional
+  argument {\tt ordered = TRUE} to order the factors in the vector:
+\begin{verbatim}  
+> x <- c("like", "dislike", "hate", "like", "don't know", "like", "dislike")
+> factor(x, levels = c("hate", "dislike", "like", "don't know"),
++        ordered = TRUE)
+  [1] like    dislike    hate    like   don't know   like   dislike   
+Levels: hate < dislike < like < don't know
+\end{verbatim}
+  The {\tt factor()} command orders the levels according to the order in
+  the optional argument {\tt levels}.  If you omit the levels command,
+  R will order the values as they occur in the vector.  Thus, omitting
+  the {\tt levels} argument sorts the levels as {\tt like < dislike <
+    hate < don't know} in the example above.  If you omit one or more
+  of the levels in the list of levels, R returns levels values of {\tt
+    NA} for the missing level(s):
+\begin{verbatim}
+> factor(x, levels = c("hate", "dislike", "like"), ordered = TRUE)
+  [1] like    dislike hate    like    <NA>    like    dislike
+Levels: hate < dislike < like
+\end{verbatim}
+Use factored vectors within data frames for plotting (see
+\Sref{ss:draw}), to set the values of the explanatory variables using
+{\tt setx} (see \Sref{s:main}) and in the ordinal logit and multinomial
+logit models (see \Sref{s:models}).  
+
+\item Build {\bf matrices} (or two-dimensional arrays) from vectors
+(one-dimensional arrays).  You can create a matrix in two ways:   
+\begin{enumerate}
+\item From a vector: Use the command \texttt{matrix(vector, nrow
+    =} $k$\texttt{, ncol =} $n$\texttt{)} to create a $k \times n$
+    matrix from the vector by filling in the columns from left to
+    right.  For example,
+\begin{verbatim}
+> matrix(c(1,2,3,4,5,6), nrow = 2, ncol = 3)
+        [,1] [,2] [,3]       # Note that when assigning a vector to a
+   [1,]    1    3    5       #  matrix, none of the rows or columns 
+   [2,]    2    4    6       #  have names.  
+\end{verbatim}
+\item From two or more vectors of length $k$: Use \texttt{cbind()} to
+  combine $n$ vectors vertically to form a $k \times n$ matrix, 
+  or \texttt{rbind()} to combine $n$ vectors horizontally to form a
+  $n \times k$ matrix.  For example:
+\begin{verbatim}
+> x <- c(11, 12, 13)         # Creates a vector `x' of 3 values.
+> y <- c(55, 33, 12)         # Creates another vector `y' of 3 values.  
+> rbind(x, y)                # Creates a 2 x 3 matrix.  Note that row
+     [,1] [,2] [,3]          #  1 is named x and row 2 is named y, 
+   x   11   12   13          #  according to the order in which the
+   y   55   33   12          #  arguments were passed to rbind().
+> cbind(x, y)                # Creates a 3 x 2 matrix.  Note that the
+         x  y                #  columns are named according to the
+   [1,] 11 55                #  order in which they were passed to
+   [2,] 12 33                #  cbind().  
+   [3,] 13 12
+\end{verbatim}
+\end{enumerate}
+R supports a variety of matrix functions, including: \texttt{det()},
+which returns the matrix's determinant; \texttt{t()}, which transposes
+the matrix; \texttt{solve()}, which inverts the the matrix; and
+\texttt{\%*\%}, which multiplies two matricies.  In addition, the
+\texttt{dim()} command returns the dimensions of your matrix.  As with
+vectors, square brackets extract specific values from a matrix and the
+assignment mechanism \texttt{<-} replaces values.  For example:
+\begin{verbatim}
+> loo[,3]     		  # Extracts the third column of loo.  
+> loo[1,]      		  # Extracts the first row of loo.  
+> loo[1,3] <- 13          # Inserts 13 as the value for row 1, column 3.  
+> loo[1,] <- c(2,2,3)     # Replaces the first row of loo.  
+\end{verbatim}
+If you encounter problems replacing rows or columns, make sure that
+the \texttt{dims()} of the vector matches the \texttt{dims()} of the
+matrix you are trying to replace.  
+
+\item An \textbf{n-dimensional array} is a set of stacked matrices of identical
+  dimensions.  For example, you may create a three dimensional array
+  with dimensions $(x, y, z)$ by stacking $z$ matrices each with $x$
+  rows and $y$ columns.  
+\begin{verbatim}
+> a <- matrix(8, 2, 3)       # Creates a 2 x 3 matrix populated with 8's.
+> b <- matrix(9, 2, 3)       # Creates a 2 x 3 matrix populated with 9's.
+> array(c(a, b), c(2, 3, 2)) # Creates a 2 x 3 x 2 array with the first
+   , , 1                     #  level [,,1] populated with matrix a (8's),
+                             #  and the second level [,,2] populated 
+        [,1] [,2] [,3]       #  with matrix b (9's).  
+   [1,]    8    8    8       
+   [2,]    8    8    8       # Use square brackets to extract values.  For
+                             #  example, [1, 2, 2] extracts the second
+   , , 2                     #  value in the first row of the second level.
+                             # You may also use the <- operator to 
+        [,1] [,2] [,3]       #  replace values.  
+   [1,]    9    9    9
+   [2,]    9    9    9
+\end{verbatim}
+If an array is a one-dimensional vector or two-dimensional matrix, R
+  will treat the array using the more specific method.  
+\end{enumerate}
+
+Three functions especially helpful for
+arrays:  
+\begin{itemize}
+\item {\tt is()} returns both the type of scalar value that populates
+the array, as well as the specific type of array (vector, matrix, or
+array more generally).
+\item {\tt dims()} returns the size of an array, where 
+\begin{verbatim}
+> dims(b) 
+ [1]  33  5
+\end{verbatim} 
+indicates that the array is two-dimensional (a matrix), and has 33
+rows and 5 columns.  
+\item The single bracket \verb|[ ]| indicates specific values in the
+array.  Use commas to indicate the index of the specific values you
+would like to pull out or replace:  
+\begin{verbatim}
+> dims(a)
+ [1]  14
+> a[10]       # Pull out the 10th value in the vector `a'
+> dims(b) 
+ [1]  33  5
+> b[1:12, ]   # Pull out the first 12 rows of `b' 
+> c[1, 2]     # Pull out the value in the first row, second column of `c'
+> dims(d)
+ [1]  1000  4  5
+> d[ , 3, 1]  # Pulls out a vector of 1,000 values 
+\end{verbatim}
+\end{itemize} 
+
+\subsection{Lists}
+
+Unlike arrays, which contain only one type of scalar value, lists
+are flexible data structures that can contain heterogeneous value types
+and heterogeneous data structures.  Lists are so flexible that one list
+can contain another list.  For example, the list {\tt output} can
+contain {\tt coef}, a vector of regression coefficients; {\tt
+variance}, the variance-covariance matrix; and another list {\tt terms}
+that describes the data using character strings.  Use the {\tt
+names()} function to view the named elements in a list, and to extract
+a named element, use
+\begin{verbatim}
+> names(output)
+ [1] coefficients   variance   terms
+> output$coefficients
+\end{verbatim} %$
+For lists where the elements are not named, use double square brackets
+\verb|[[ ]]| to extract elements:  
+\begin{verbatim}
+> L[[4]]      # Extracts the 4th element from the list `L'
+> L[[4]] <- b # Replaces the 4th element of the list `L' with a matrix `b'
+\end{verbatim}
+
+Like vectors, lists are flexible data structures that can be extended
+without first creating another list of with the correct number of
+elements:  
+\begin{verbatim}
+> L <- list()                      # Creates an empty list
+> L$coefficients <- c(1, 4, 6, 8)  # Inserts a vector into the list, and 
+                                   #  names that vector `coefficients' 
+				   #  within the list
+> L[[4]] <- c(1, 4, 6, 8)          # Inserts the vector into the 4th position
+                                   #  in the list.  If this list doesn't 
+                                   #  already have 4 elements, the empty 
+                                   #  elements will be `NULL' values
+\end{verbatim} %$
+Alternatively, you can easily create a list using objects that already
+exist in your workspace:  
+\begin{verbatim}
+> L <- list(coefficients = k, variance = v) # Where `k' is a vector and
+                                            #   `v' is a matrix
+\end{verbatim}
+
+\subsection{Data Frames}
+
+A data frame (or data set) is a special type of list in which each
+variable is constrained to have the same number of observations.  A
+data frame may contain variables of different types (numeric,
+integer, logical, character, and factor), so long as each variable has
+the same number of observations. 
+
+Thus, a data frame can use both matrix commands and list commands to
+manipulate variables and observations.  
+\begin{verbatim}
+> dat[1:10,]         # Extracts observations 1-10 and all associated variables  
+> dat[dat$grp == 1,] # Extracts all observations that belong to group 1 
+> group <- dat$grp   # Saves the variable `grp' as a vector `group' in
+                     #   the workspace, not in the data frame
+> var4 <- dat[[4]]   # Saves the 4th variable as a `var4' in the workspace
+\end{verbatim} 
+
+For a comprehensive introduction to data frames and recoding data, see
+\Sref{load.data}.
+
+\subsection{Identifying Objects and Data Structures}
+
+Each data structure has several \emph{attributes} which describe it.
+Although these attributes are normally invisible to users (e.g., not
+printed to the screen when one types the name of the object), there are
+several helpful functions that display particular attributes:  
+\begin{itemize}
+\item For arrays, {\tt dims()} returns the size of each dimension.  
+\item For arrays, {\tt is()} returns the scalar value type and
+specific type of array (vector, matrix, array).  For more complex data
+structures, {\tt is()} returns the default methods (classes) for that object. 
+\item For lists and data frames, {\tt names()} returns the variable
+names, and {\tt str()} returns the variable names and a short
+description of each element.  
+\end{itemize}  
+For almost all data types, you may use {\tt summary()} to get summary
+statistics.  
+
+\chapter{Programming Statements}
+
+This chapter introduces the main programming commands.  These include
+functions, if-else statements, for-loops, and special procedures for
+managing the inputs to statistical models.  
+
+\section{Functions}
+
+Functions are either built-in or user-defined sets of encapsulated
+commands which may take any number of arguments.  Preface a function
+with the {\tt function} statement and use the {\tt <-}
+operator to assign functions to objects in your workspace.  
+
+You may use functions to run the same procedure on different objects
+in your workspace.  For example, 
+\begin{verbatim}
+check <- function(p, q) { 
+ result <- (p - q)/q
+ result
+ }
+\end{verbatim}
+is a simple function with arguments {\tt p} and {\tt q} which
+calculates the difference between the $i$th elements of the vector
+{\tt p} and the $i$th element of the vector {\tt q} as a proportion of
+the $i$th element of {\tt q}, and returns the resulting vector.  For
+example, {\tt check(p = 10, q = 2)} returns 4.  You may omit the
+descriptors as long as you keep the arguments in the correct order:
+{\tt check(10, 2)} also returns 4.  You may also use other objects as
+inputs to the function.  If {\tt again = 10} and {\tt really = 2},
+then {\tt check(p = again, q = really)} and {\tt check(again, really)}
+also returns 4.
+
+Because functions run commands as a set, you should make sure that
+each command in your function works by testing each line of the
+function at the R prompt.
+
+\section{If-Statements}
+
+Use {\tt if} (and optionally, {\tt else}) to control the flow of R
+functions.  For example, let {\tt x} and {\tt y} be scalar numerical
+values:  
+\begin{verbatim}
+if (x == y) {                # If the logical statement in the ()'s is true,  
+  x <- NA                    #  then `x' is changed to `NA' (missing value). 
+}
+else {                       # The `else' statement tells R what to do if  
+  x <- x^2                   #  the if-statement is false.  
+} 
+\end{verbatim}
+As with a function, use {\tt \{} and {\tt \}} to define the set of commands associated
+with each if and else statement.  (If you include if
+statements inside functions, you may have multiple sets of nested
+curly braces.)
+
+\section{For-Loops}
+
+Use {\tt for} to repeat (loop) operations.  Avoiding loops by using matrix
+or vector commands is usually faster and more elegant, but loops are
+sometimes necessary to assign values.  If you are using a loop to
+assign values to a data structure, you must first initialize an empty
+data structure to hold the values you are assigning.
+
+Select a data structure compatible with the type of output your loop
+will generate.  If your loop generates a scalar, store it in a vector
+(with the $i$th value in the vector corresponding to the the $i$th run
+of the loop).  If your loop generates vector output, store them as
+rows (or columns) in a matrix, where the $i$th row (or column)
+corresponds to the $i$th iteration of the loop.  If your output
+consists of matrices, stack them into an array.  For list output (such
+as regression output) or output that changes dimensions in each
+iteration, use a list.  To initialize these data structures, use:
+\begin{verbatim}
+> x <- vector()                          # An empty vector of any length.
+> x <- list()                            # An empty list of any length.  
+\end{verbatim}
+The {\tt vector()} and {\tt list()} commands create a vector or list
+of any length, such that assigning {\tt x[5] <- 15} automatically
+creates a vector with 5 elements, the first four of which are empty
+values ({\tt NA}).  In contrast, the {\tt matrix()} and {\tt array()}
+commands create data structures that are restricted to their original
+dimensions.  
+\begin{verbatim}
+> x <- matrix(nrow = 5, ncol = 2)  # A matrix with 5 rows and 2 columns.
+> x <- array(dim = c(5,2,3))       # A 3D array of 3 stacked 5 by 2 matrices.
+\end{verbatim}
+If you attempt to assign a value at $(100, 200, 20)$ to either of
+these data structures, R will return an error message (``subscript is
+out of bounds'').  R does not automatically extend the
+dimensions of either a matrix or an array to accommodate additional
+values.  
+
+\paragraph{Example 1: Creating a vector with a logical statement} 
+\begin{verbatim}
+x <- array()             # Initializes an empty data structure.  
+for (i in 1:10) {        # Loops through every value from 1 to 10, replacing
+  if (is.integer(i/2)) { #  the even values in `x' with i+5.
+    x[i] <- i + 5 
+  }      
+}                        # Enclose multiple commands in {}.  
+\end{verbatim}
+You may use {\tt for()} inside or outside of functions.  
+
+\paragraph{Example 2: Creating dummy variables by hand}  
+
+\label{dummy}You may also use a loop to create a matrix of dummy
+variables to append to a data frame.  For example, to generate fixed
+effects for each state, let's say that you have {\tt mydata} which
+contains {\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with
+{\tt state} a character variable with 50 unique values.  There are
+three ways to create dummy variables: 1) with a built-in R command; 2)
+with one loop; or 3) with 2 for loops.  
+\begin{enumerate}
+\item R will create dummy variables on the fly from a single variable
+with distinct values.  
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2 + x3 + as.factor(state), 
+                 data = mydata, model = "ls")
+\end{verbatim}
+This method returns $k - 1$ indicators for $k$ states.  
+
+\item Alternatively, you can use a loop to create dummy variables by
+hand.  There are two ways to do this, but both start with the same
+initial commands. Using vector commands, first create an index of for
+the states, and initialize a matrix to hold the dummy variables:
+\begin{verbatim}  
+idx <- sort(unique(mydata$state))
+dummy <- matrix(NA, nrow = nrow(mydata), ncol = length(idx))
+\end{verbatim}  %$
+Now choose between the two methods.  
+\begin{enumerate}
+\item The first method is computationally inefficient, but more intuitive for users not
+accustomed to vector operations.  The first loop uses {\tt i} as in
+index to loop through all the rows, and the second loop uses {\tt j}
+to loop through all 50 values in the vector {\tt idx}, which
+correspond to columns 1 through 50 in the matrix {\tt dummy}.
+\begin{verbatim}
+for (i in 1:nrow(mydata)) {
+  for (j in 1:length(idx)) {
+    if (mydata$state[i,j] == idx[j]) {
+      dummy[i,j] <- 1
+    }
+    else {
+      dummy[i,j] <- 0
+    }
+  }
+}
+\end{verbatim}  % $
+Then add the new matrix of dummy variables to your data frame:
+\begin{verbatim}
+names(dummy) <- idx
+mydata <- cbind(mydata, dummy) 
+\end{verbatim}  
+
+\item As you become more comfortable with vector operations, you can
+replace the double loop procedure above with one loop:   
+\begin{verbatim}
+for (j in 1:length(idx)) { 
+  dummy[,j] <- as.integer(mydata$state == idx[j])
+}
+\end{verbatim} %$
+The single loop procedure evaluates each element in {\tt idx} against the vector {\tt mydata\$state}.  This creates a vector of $n$ {\tt
+    TRUE}/{\tt FALSE} observations, which you may transform to {\tt
+    1}'s and {\tt 0}'s using {\tt as.integer()}.  Assign the resulting
+  vector to the appropriate column in {\tt dummy}.  Combine the {\tt
+dummy} matrix with the data frame as above to complete the procedure.
+\end{enumerate}
+\end{enumerate}
+
+\paragraph{Example 3: Weighted regression with subsets}  
+
+Selecting the {\tt by} option in {\tt zelig()} partitions the data
+frame and then automatically loops the specified model through each
+partition.  Suppose that {\tt mydata} is a data frame with variables
+{\tt y}, {\tt x1}, {\tt x2}, {\tt x3}, and {\tt state}, with {\tt
+state} a factor variable with 50 unique values.  Let's say that you
+would like to run a weighted regression where each observation is
+weighted by the inverse of the standard error on {\tt x1}, estimated
+for that observation's state.  In other words, we need 
+to first estimate the model for each of the 50 states, calculate 1 /
+{\sc se}({\tt x1}$_j$) for each state $j = 1, \dots, 50$, and then
+assign these weights to each observation in {\tt mydata}.    
+\begin{itemize}
+\item Estimate the model separate for each state using the {\tt by}
+option in {\tt zelig()}:  
+\begin{verbatim}
+z.out <- zelig(y ~ x1 + x2 + x3, by = "state", data = mydata, model = "ls")
+\end{verbatim}
+Now {\tt z.out} is a list of 50 regression outputs.  
+\item Extract the standard error on {\tt x1} for each of the state
+level regressions.  
+\begin{verbatim}
+se <- array()                          # Initalize the empty data structure.
+for (i in 1:50) {                      # vcov() creates the variance matrix
+  se[i] <- sqrt(vcov(z.out[[i]])[2,2]) # Since we have an intercept, the 2nd 
+}                                      # diagonal value corresponds to x1.
+\end{verbatim}
+\item Create the vector of weights.  
+\begin{verbatim}
+wts <- 1 / se
+\end{verbatim}
+This vector {\tt wts} has 50 values that correspond to the 50 sets of
+state-level regression output in {\tt z.out}.  
+\item To assign the vector of weights to each observation, we need to
+match each observation's state designation to the appropriate state.
+For simplicity, assume that the states are numbered 1 through 50.
+\begin{verbatim}
+mydata$w <- NA            # Initalizing the empty variable
+for (i in 1:50) { 
+  mydata$w[mydata$state == i] <- wts[i]
+} 
+\end{verbatim} %$
+We use {\tt mydata\$state} as the index (inside the square brackets)
+to assign values to {\tt mydata\$w}.  Thus, whenever state equals 5
+for an observation, the loop assigns the fifth value in the vector
+{\tt wts} to the variable {\tt w} in {\tt mydata}.  If we had 500
+observations in {\tt mydata}, we could use this method to match each
+of the 500 observations to the appropriate {\tt wts}.
+
+If the states are character strings instead of integers, we can use a
+slightly more complex version
+\begin{verbatim}
+mydata$w <- NA
+idx <- sort(unique(mydata$state))
+for (i in 1:length(idx) { 
+  mydata$w[mydata$state == idx[i]] <- wts[i]
+}
+\end{verbatim}   
+
+\item Now we can run our weighted regression:  
+\begin{verbatim}
+z.wtd <- zelig(y ~ x1 + x2 + x3, weights = w, data = mydata, 
+               model = "ls")
+\end{verbatim}
+
+\end{itemize}  
+
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "zelig"
+%%% End: 
diff --git a/inst/doc/announce.txt b/inst/doc/announce.txt
new file mode 100644
index 0000000..c40e466
--- /dev/null
+++ b/inst/doc/announce.txt
@@ -0,0 +1,68 @@
+
+                Zelig: Everyone's Statistical Software
+
+                Kosuke Imai, Gary King and Olivia Lau
+
+                             Version 2.0
+
+            (Available at http://gking.harvard.edu/zelig)
+
+
+A growing proportion of statisticians and methodologists from many
+disciplines are converging on R, a powerful statistics package and
+programming language.  As an open source project, R is freely
+accessible.  With thousands of contributors who have written hundreds
+of packaged routines, R can deal with nearly any statistical problem.
+Although this high level of participation may be its greatest
+strength, the enormous diversity in approaches to statistical analyses
+and theories of statistical inference covered by R packages often
+results in a virtual babel of contradictory statistical advice,
+competing functions, and inconsistent syntax.
+
+To address these problems from a common perspective, we have created
+Zelig, a single, easy-to-use program, with a unified framework and
+syntax, that can estimate, help interpret, and present the results of
+a large range of statistical methods. It literally _is_ "everyone's
+statistical software" because Zelig uses R code from many researchers.
+We also hope it will _become_ "everyone's statistical software" for
+applications, and we have designed it so that anyone can use it or add
+their methods to it.  Zelig comes with detailed, self-contained
+documentation that minimizes startup costs for Zelig and R, automates
+graphics and summaries for all models, and, with only three simple
+commands required, generally makes the power of R accessible for all
+users.  Zelig also works well for teaching, and is designed so that
+scholars can use the same program they use for their research.
+
+Zelig adds considerable infrastructure to improve the use of existing
+methods.  Zelig
+ - implements and generalizes the program Clarify (for Stata),
+   which translates hard-to-interpret coefficients into quantities of
+   interest;
+ - combines multiply imputed data sets (such as output from Amelia) to
+   deal with missing data;
+ - automates bootstrapping for all models; 
+ - uses sophisticated nonparametric matching commands which improve
+   parametric procedures (via MatchIt);
+ - allows one-line commands to run analyses in all designated strata;
+ - automates the creation of replication data files so that you (or,
+   if you wish, anyone else) can replicate the results of your
+   analyses (hence satisfying the replication standard); and
+ - makes conditional population and superpopulation inferences.
+ 
+If you wish to add your methods to Zelig, the documentation also
+includes simple instructions for how to do so.  The process is easy
+and quick, and can be done by writing a few bridge functions and
+without rewriting or "porting" your R code.  If you don't know how to
+program in R, the Zelig documentation provides all the information you
+need.  R also interfaces seamlessly with C and Fortran if you prefer
+to write in those languages.  A variety of the best methodologists are
+currently working to include their methods in Zelig.  As Zelig grows,
+you will have access to an increasing range of cutting edge methods
+and models.
+
+You may also be interested in an APSA short course on Zelig, held on
+Wednesday, September 1st, at 1pm.  There is no fee, but
+preregistration is required; please email Olivia Lau
+<olau at fas.harvard.edu> to sign up.
+
+
diff --git a/inst/doc/commands/describe.mymodel.tex b/inst/doc/commands/describe.mymodel.tex
new file mode 100644
index 0000000..e2d4705
--- /dev/null
+++ b/inst/doc/commands/describe.mymodel.tex
@@ -0,0 +1,206 @@
+\section{{\tt describe}: Describe a model's systematic and stochastic 
+parameters}
+\label{describe.mymodel}
+
+\subsubsection{Description}
+
+In order to use {\tt parse.formula()}, {\tt parse.par()}, and the {\tt
+model.*.multiple()} commands, you must write a {\tt describe.mymodel()}
+function where {\tt mymodel} is the name of your modeling function.
+(Hence, if your function is called {\tt normal.regression()}, you need
+to write a {\tt describe.normal.regression()} function.)  Note that
+{\tt describe()} is \emph{not} a generic function, but is called by
+{\tt parse.formula(\dots, model = "mymodel")} using a combination of
+{\tt paste()} and {\tt exists()}.  You will never need to call {\tt
+describe.mymodel()} directly, since it will be called from {\tt
+parse.formula()} as that function checks the user-input formula or
+list of formulas.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+describe.mymodel()
+\end{verbatim}
+
+\subsubsection{Arguments}\label{categories}
+The {\tt describe.mymodel()} function takes no arguments.  
+
+\subsubsection{Output Values}
+The {\tt describe.mymodel()} function returns a list with the
+following information:  
+\begin{itemize}
+\item {\tt category}: a character string, consisting of one of the
+following: 
+\begin{itemize}
+\item {\tt "continuous"}: the dependent variable is continuous, numeric, and
+unbounded (e.g., normal regression), but may be censored with an associated censoring 
+indicator (e.g., tobit regression).  
+\item {\tt "dichotomous"}: the dependent variable takes two discrete integer
+values, usually 0 and 1 (e.g., logistic regression).  
+\item {\tt "ordinal"}: the dependent variable is an ordered factor
+response, taking 3 or more discrete values which are arranged in an
+ascending or descending manner (e.g., ordered logistic regression).  
+\item {\tt "multinomial"}: the dependent variable is an unordered
+factor response, taking 3 or more discrete values which are arranged
+in no particular order (e.g., multinomial logistic regression).  
+\item {\tt "count"}: the dependent variable takes integer values
+greater than or equal to 0 (e.g., Poisson regression).  
+\item {\tt "bounded"}: the dependent variable is a continuous numeric variable, that 
+is restricted (bounded within) some range (e.g., $(0, \infty)$).  The variable may 
+also be censored either on the left and/or right side, with an associated censoring 
+indicator (e.g., Weibull regression).
+\item {\tt "mixed"}: the dependent variables are a mix of the above
+categories (usually applies to multiple equation models).  
+\end{itemize}
+Selecting the category is particularly important since it sets certain
+interface parameters for the entire GUI.
+
+\item {\tt package}: (optional) a list with the following elements
+
+  \begin{itemize} 
+
+   \item {\tt name}: a characters string with the name of the package
+   containing the {\tt mymodel()} function.
+
+   \item {\tt version}: the minimum version number that works with
+   Zelig.
+
+   \item {\tt CRAN}: if the package is not hosted on CRAN mirrors,
+   provide the URL here as a character string.  You should be able
+   to install your package from this URL using {\tt name}, {\tt
+version}, and {\tt CRAN}:
+\begin{verbatim}
+install.packages(name, repos = CRAN, installWithVers = TRUE)
+\end{verbatim}  
+By default, {\tt CRAN = "http://cran.us.r-project.org/"}.  
+\end{itemize}
+
+\item {\tt parameters}: For each systematic and stochastic parameter
+(or set of parameters) in your model, you should create a list (named
+after the parameters as given in your model's notation, e.g., {\tt
+mu}, {\tt sigma}, {\tt theta}, etc.; not literally {\tt myparameter})
+with the following information:
+\begin{itemize}
+
+\item {\tt equations}: an integer number of equations for the
+parameter.  For parameters that can take an undefined number of
+equations (for example in seemingly unrelated regression), use {\tt
+c(2, Inf)} or {\tt c(2, 999)} to indicate that the parameter can take
+a minimum of two equations up to a theoretically infinite number of
+equations.  
+
+\item {\tt tagsAllowed}: a logical value ({\tt TRUE}/{\tt FALSE})
+specifying whether a given parameter allows constraints.  If there is
+only one equation for a parameter (for example, {\tt mu} for the
+normal regression model has {\tt equations = 1}), then {\tt
+tagsAllowed = FALSE} by default.  If there are two or more equations
+for the parameter (for example, {\tt mu} for the bivariate probit
+model has {\tt equations = 2}), then {\tt tagsAllowed = TRUE} by
+default.  
+
+\item {\tt depVar}: a logical value ({\tt TRUE}/{\tt FALSE})
+specifying whether a parameter requires a corresponding dependent
+variable.  
+
+\item {\tt expVar}: a logical value ({\tt TRUE}/{\tt FALSE})
+specifying whether a parameter allows explanatory variables.  If {\tt
+depVar = TRUE} and {\tt expVar = TRUE}, we call the parameter a
+``systematic component'' and {\tt parse.formula()} will fail if
+formula(s) are not specified for this parameter.  If {\tt
+depVar = FALSE} and {\tt expVar = TRUE}, the parameter is estimated as
+a scalar ancillary parameter, with default formula \verb|~ 1|, if the
+user does not specify a formula explicitly.  If {\tt depVar = FALSE}
+and {\tt expVar = FALSE}, the parameter can only be estimated as a
+scalar ancillary parameter.  
+
+\item {\tt specialFunction}: (optional) a character string giving the
+name of a function that appears on the left-hand side of the formula.
+Options include {\tt "Surv"}, {\tt "cbind"}, and {\tt "as.factor"}. 
+
+\item {\tt varInSpecial}: (optional) a scalar or vector giving the
+number of variables taken by the {\tt specialFunction}.  For example,
+{\tt Surv()} takes a minimum of 2 arguments, and a maximum of 4
+arguments, which is represented as {\tt c(2, 4)}.   
+
+\end{itemize}
+If you have more than one parameter (or set of parameters) in
+your model, you will need to produce a {\tt myparameter} list for each
+one.  See examples below for details.  
+\end{itemize}
+
+\subsubsection{Examples}
+For a Normal regression model with mean {\tt mu} and scalar variance
+parameter {\tt sigma2}, the minimal {\tt describe.*()} function is as
+follows:  
+\begin{verbatim}
+describe.normal.regression <- function() {
+  category <- "continuous"
+  mu <- list(equations = 1,              # Systematic component
+             tagsAllowed = FALSE, 
+             depVar = TRUE, 
+             expVar = TRUE)
+  sigma2 <- list(equations = 1,          # Scalar ancillary parameter
+                 tagsAllowed = FALSE, 
+                 depVar = FALSE, 
+                 expVar = FALSE)
+  pars <- list(mu = mu, sigma2 = sigma2)
+  model <- list(category = category, parameters = pars)
+}
+\end{verbatim}
+See \Sref{normal.regression} for full code to execute this model from
+scratch in R with Zelig.  
+
+Now consider a bivariate probit model with parameter vector {\tt mu} and
+correlation parameter {\tt rho} (which may or may not take explanatory
+variables).  Since the bivariate probit function uses the {\tt pmvnorm()} 
+function from the mvtnorm library, we list this under {\tt package}.   
+\begin{verbatim}
+describe.bivariate.probit <- function() {
+  category <- "dichotomous"
+  package <- list(name = "mvtnorm", 
+                  version = "0.7")
+  mu <- list(equations = 2,               # Systematic component 
+             tagsAllowed = TRUE,          
+             depVar = TRUE, 
+             expVar = TRUE) 
+  rho <- list(equations = 1,              # Optional systematic component
+             tagsAllowed = FALSE,         #   Estimated as an ancillary
+             depVar = FALSE,              #   parameter by default
+             expVar = TRUE) 
+  pars <- list(mu = mu, rho = rho)
+  list(category = category, package = package, parameters = pars)
+}
+\end{verbatim}  
+See \Sref{bivariate.probit} for the full code to write this model from
+scratch in R with Zelig. 
+
+For a multinomial logit model, which takes an undefined number of
+equations (corresponding to each level in the response variable):  
+\begin{verbatim}
+describe.multinomial.logit <- function() { 
+  category <- "multinomial"
+  mu <- list(equations = c(1, Inf), 
+             tagsAllowed = TRUE, 
+             depVAR = TRUE, 
+             expVar = TRUE, 
+             specialFunction <- "as.factor", 
+             varInSpecial <- c(1, 1))
+  list(category = category, parameters = list(mu = mu))
+}
+\end{verbatim}
+(This example does not have corresponding executable sample code.)
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{s:new} for an overview of how the {\tt describe.*()}
+function works with {\tt parse.formula()}.  
+\item \Sref{parse.formula} for information on {\tt parse.formula()}.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/commands/matchit.tex b/inst/doc/commands/matchit.tex
new file mode 100644
index 0000000..d8d38f2
--- /dev/null
+++ b/inst/doc/commands/matchit.tex
@@ -0,0 +1,29 @@
+\section{{\tt matchit}: Create matched data}\label{matchit}
+
+\subsubsection{Description}
+
+\hlink{MatchIt}{http://gking.harvard.edu/matchit/} implements the
+suggestions of \cite{HoImaKin05b} for improving parametric statistical
+models by preprocessing data with semi-parametric matching methods. It
+uses a sophisticated array of matching methods to select well-matched
+treated and control units from the original data set, thus reducing
+the dependence of causal inferences on functional form and other
+parametric assumptions.  After pre-processing, MatchIt output can be
+used just like any other dataset in Zelig to estimate causal effects.
+In this way, MatchIt improves rather than replaces existing parametric
+models, reducing sensitivity to modeling assumptions.  The matching
+methods available in
+\hlink{MatchIt}{http://gking.harvard.edu/matchit/} include exact
+matching on all covariates, nearest neighbor matching,
+subclassification, optimal matching, genetic matching, and full
+matching.  An outline of all options are provided below; see the full
+documentation (available at
+\hlink{\url{http://gking.harvard.edu/matchit/}}{http://gking.harvard.edu/matchit/})
+for more details.
+
+\input{commands/matchitref}
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "zelig"
+%%% End: 
diff --git a/inst/doc/commands/matchitref.tex b/inst/doc/commands/matchitref.tex
new file mode 100644
index 0000000..a5e4e2d
--- /dev/null
+++ b/inst/doc/commands/matchitref.tex
@@ -0,0 +1,390 @@
+\subsubsection{Syntax}
+\begin{verbatim}
+> m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\paragraph{Arguments for All Matching Methods}
+
+\begin{itemize}
+
+\item \texttt{formula}: formula used to calculate the distance measure
+  for matching.  It takes the usual syntax of R formulas, {\tt treat
+    \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator,
+  and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the
+  treatment indicator and pre-treatment covariates must be contained
+  in the same data frame, which is specified as {\tt data} (see
+  below).  All of the usual R syntax for formulas work here. For
+  example, {\tt x1:x2} represents the first order interaction term
+  between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the
+  square term of {\tt x1}. See {\tt help(formula)} for details.
+  
+\item \texttt{data}: the data frame containing the variables called in
+  {\tt formula}.  
+  
+\item \texttt{method}: the matching method (default =
+  \texttt{"nearest"}, nearest neighbor matching).  Currently,
+  \texttt{"exact"} (exact matching), \texttt{"full"} (full matching),
+  \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"}
+  (optimal matching), \texttt{"subclass"} (subclassification), and
+  \texttt{"genetic"} (genetic matching) are available. Note that
+  within each of these matching methods, \MatchIt\ offers a variety of
+  options. See below for more details.
+  
+\item \texttt{verbose}: a logical value indicating whether to print
+  the status of the matching algorithm (default = \texttt{FALSE}).
+\end{itemize}
+
+
+\paragraph{Additional Arguments for Specification of
+  Distance Measures}
+\label{subsubsec:inputs-all}
+
+The following arguments specify distance measures that are used for
+matching methods. These arguments apply to all matching methods {\it
+  except exact matching}.
+
+\begin{itemize}
+  
+\item \texttt{distance}: the method used to estimate the distance
+  measure (default = {\tt "logit"}, logistic regression) or a
+  numerical vector of user's own distance measure.  Before using any
+  of these techniques, it is best to understand the theoretical
+  groundings of these techniques and to evaluate the results.  Most of
+  these methods (such as logistic or probit regression) estimate the
+  propensity score, defined as the probability of receiving treatment,
+  conditional on the covariates.  Available methods include:
+  \begin{itemize}
+  \item {\tt "mahalanobis"}: the Mahalanobis distance measure.
+  \item binomial generalized linear models with one of the following
+    link functions:
+    \begin{itemize}
+    \item \texttt{"logit"}: logistic link 
+    \item {\tt "linear.logit"}: logistic link with linear propensity
+      score)\footnote{The linear propensity scores are obtained by
+        transforming back onto a linear scale.}
+    \item \texttt{"probit"}: probit link
+    \item {\tt "linear.probit"}: probit link with linear propensity
+      score
+    \item {\tt "cloglog"}: complementary log-log link
+    \item {\tt "linear.cloglog"}: complementary log-log link with linear
+      propensity score
+    \item {\tt "log"}: log link
+    \item {\tt "linear.log"}: log link with linear propensity score
+    \item {\tt "cauchit"} Cauchy CDF link
+    \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity
+      score
+    \end{itemize}
+  \item Choose one of the following generalized additive models (see
+    {\tt help(gam)} for more options).
+    \begin{itemize}
+    \item \texttt{"GAMlogit"}: logistic link
+    \item {\tt "GAMlinear.logit"}: logistic link with linear propensity
+      score
+    \item \texttt{"GAMprobit"}: probit link
+    \item {\tt "GAMlinear.probit"}: probit link with linear propensity
+      score
+    \item {\tt "GAMcloglog"}: complementary log-log link 
+    \item {\tt "GAMlinear.cloglog"}: complementary log-log link with
+      linear propensity score
+    \item {\tt "GAMlog"}: log link
+    \item {\tt "GAMlinear.log"}: log link with linear propensity score,
+    \item {\tt "GAMcauchit"}: Cauchy CDF link
+    \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear
+      propensity score
+\end{itemize} 
+\item \texttt{"nnet"}: neural network model.  See {\tt help(nnet)} for
+  more options.
+\item \texttt{"rpart"}: classification trees.  See {\tt help(rpart)}
+  for more options.
+  \end{itemize}
+  
+\item \texttt{distance.options}: optional arguments for estimating the
+  distance measure. The input to this argument should be a list.  For
+  example, if the distance measure is estimated with a logistic
+  regression, users can increase the maximum IWLS iterations by
+  \texttt{distance.options = list(maxit = 5000)}.  Find additional
+  options for general linear models using {\tt help(glm)} or {\tt
+    help(family)}, for general additive models using {\tt help(gam)},
+  for neutral network models {\tt help(nnet)}, and for classification
+  trees {\tt help(rpart)}.
+  
+\item \texttt{discard}: specifies whether to discard units that fall
+  outside some measure of support of the distance measure (default =
+  \texttt{"none"}, discard no units).  Discarding units may change the
+  quantity of interest being estimated. Enter a logical vector
+  indicating which unit should be discarded or choose from the
+  following options:
+  \begin{itemize}
+  \item \texttt{"none"}: no units will be discarded before matching.
+    Use this option when the units to be matched are substantially
+    similar, such as in the case of matching treatment and control
+    units from a field experiment that was close to (but not fully)
+    randomized (e.g., \citealt{Imai05}), when caliper matching will
+    restrict the donor pool, or when you do not wish to change the
+    quantity of interest and the parametric methods to be used
+    post-matching can be trusted to extrapolate.
+  \item \texttt{"hull.both"}: all units that are not within the convex
+    hull will be discarded.  We recommend that this option be used
+    with observational data sets.
+  \item \texttt{"both"}: all units (treated and control) that are
+    outside the support of the distance measure will be discarded.
+  \item \texttt{"hull.control"}: only control units that are not
+    within the convex hull of the treated units will be discarded.  
+  \item \texttt{"control"}: only control units outside the support of
+    the distance measure of the treated units will be discarded.  Use
+    this option when the average treatment effect on the treated is of
+    most interest and when you are unwilling to discard
+    non-overlapping treatment units (which would change the quantity
+    of interest).
+  \item \texttt{"hull.treat"}: only treated units that are not within
+    the convex hull of the control units will be discarded. 
+  \item \texttt{"treat"}: only treated units outside the support of
+    the distance measure of the control units will be discarded.  Use
+    this option when the average treatment effect on the control units
+    is of most interest and when unwilling to discard control units.
+  \end{itemize}
+  
+\item \texttt{reestimate}: If {\tt FALSE} (default), the model for the
+  distance measure will not be re-estimated after units are discarded.
+  The input must be a logical value.  Re-estimation may be desirable
+  for efficiency reasons, especially if many units were discarded and
+  so the post-discard samples are quite different from the original
+  samples.
+  
+\end{itemize}
+
+\paragraph{Additional Arguments for Subclassification}
+\label{subsubsec:inputs-subclass}
+
+\begin{itemize}
+\item \texttt{sub.by}: criteria for subclassification.  Choose from:
+  \texttt{"treat"} (default), the number of treatment units;
+  \texttt{"control"}, the number of control units; or \texttt{"all"},
+  the total number of units.
+\item \texttt{subclass}: either a scalar specifying the number of
+  subclasses, or a vector of probabilities bounded between 0 and 1,
+  which create quantiles of the distance measure using the units in
+  the group specified by \texttt{sub.by} (default = \texttt{subclass =
+    6}).
+\end{itemize}
+
+\paragraph{Additional Arguments for Nearest Neighbor Matching}
+\label{subsubsec:inputs-nearest}
+
+\begin{itemize}
+\item \texttt{m.order}: the order in which to match treatment units
+  with control units.
+  \begin{itemize}
+  \item {\tt "largest"} (default): matches from the largest value of
+    the distance measure to the smallest.
+  \item {\tt "smallest"}: matches from the smallest value of the
+    distance measure to the largest.
+  \item {\tt "random"}: matches in random order.
+  \end{itemize}
+\item \texttt{replace}: logical value indicating whether each control
+  unit can be matched to more than one treated unit (default = {\tt
+    replace = FALSE}, each control unit is used at most once -- i.e.,
+  sampling without replacement). For matching with replacement, use
+  \texttt{replace = TRUE}.
+\item \texttt{ratio}: the number of control units to match to each
+  treated unit (default = {\tt 1}).  If matching is done without
+  replacement and there are fewer control units than {\tt ratio} times
+  the number of eligible treated units (i.e., there are not enough
+  control units for the specified method), then the higher ratios will
+  have \texttt{NA} in place of the matching unit number in
+  \texttt{match.matrix}.
+\item \texttt{exact}: variables on which to perform exact matching
+  within the nearest neighbor matching (default = {\tt NULL}, no exact
+  matching).  If \texttt{exact} is specified, only matches that
+  exactly match on the covariates in \texttt{exact} will be allowed.
+  Within the matches that match on the variables in \texttt{exact},
+  the match with the closest distance measure will be chosen.
+  \texttt{exact} should be entered as a vector of variable names
+  (e.g., \texttt{exact = c("X1", "X2")}).
+\item \texttt{caliper}: the number of standard deviations of the
+  distance measure within which to draw control units (default = {\tt
+    0}, no caliper matching).  If a caliper is specified, a control
+  unit within the caliper for a treated unit is randomly selected as
+  the match for that treated unit.  If \texttt{caliper != 0}, there
+  are two additional options:
+  \begin{itemize} 
+  \item \texttt{calclosest}: whether to take the nearest available
+    match if no matches are available within the \texttt{caliper}
+    (default = {\tt FALSE}).
+  \item \texttt{mahvars}: variables on which to perform
+    Mahalanobis-metric matching within each caliper (default = {\tt
+      NULL}).  Variables should be entered as a vector of variable
+    names (e.g., \texttt{mahvars = c("X1", "X2")}).  If
+    \texttt{mahvars} is specified without \texttt{caliper}, the
+    caliper is set to 0.25.
+  \end{itemize}
+\item \texttt{subclass} and \texttt{sub.by}: See the options for
+  subclassification for more details on these options.  If a
+  \texttt{subclass} is specified within \texttt{method = "nearest"},
+  the matched units will be placed into subclasses after the nearest
+  neighbor matching is completed.
+\end{itemize}
+
+\paragraph{Additional Arguments for Optimal Matching}
+\label{subsubsec:inputs-optimal}
+
+\begin{itemize}
+\item {\tt ratio}: the number of control units to be matched to each
+  treatment unit (default = {\tt 1}).
+\item {\tt ...}: additional inputs that can be passed to the {\tt
+    fullmatch()} function in the {\tt optmatch} package. See {\tt
+    help(fullmatch)} or
+  \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html}
+  for details.
+\end{itemize}
+
+\paragraph{Additional Arguments for Full Matching}
+\label{subsubsec:inputs-full}
+
+\begin{itemize}
+\item {\tt ...}: additional inputs that can be passed to the {\tt
+    fullmatch()} function in the {\tt optmatch} package. See {\tt
+    help(fullmatch)} or
+  \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details.
+\end{itemize}
+
+\paragraph{Additional Arguments for Genetic Matching}
+\label{subsubsec:inputs-genetic}
+
+The available options are listed below.
+\begin{itemize}
+\item {\tt ratio}: the number of control units to be matched to each
+  treatment unit (default = {\tt 1}).
+\item {\tt ...}: additional minor inputs that can be passed to the
+  {\tt GenMatch()} function in the {\tt Matching} package. See {\tt
+    help(GenMatch)} or
+  \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}
+  for details.
+\end{itemize}
+
+\subsubsection{Output Values}
+\label{sec:outputs}
+
+Regardless of the type of matching performed, the \texttt{matchit}
+output object contains the following elements:\footnote{When
+  inapplicable or unnecessary, these elements may equal {\tt NULL}.
+  For example, when exact matching, {\tt match.matrix = NULL}.}
+
+\begin{itemize}
+\item \texttt{call}: the original {\tt matchit()} call.
+  
+\item \texttt{formula}: the formula used to specify the model for
+  estimating the distance measure.
+  
+\item \texttt{model}: the output of the model used to estimate
+  the distance measure.  \texttt{summary(m.out\$model)} will give the
+  summary of the model where \texttt{m.out} is the output object from
+  \texttt{matchit()}.
+  
+\item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix
+  where:
+  \begin{itemize}
+  \item the row names represent the names of the treatment units
+    (which match the row names of the data frame specified in
+    \texttt{data}).
+  \item each column stores the name(s) of the control unit(s) matched
+    to the treatment unit of that row. For example, when the
+    \texttt{ratio} input for nearest neighbor or optimal matching is
+    specified as 3, the three columns of \texttt{match.matrix}
+    represent the three control units matched to one treatment unit).
+  \item \texttt{NA} indicates that the treatment unit was not matched.
+  \end{itemize}
+  
+\item \texttt{discarded}: a vector of length $n$ that displays whether
+  the units were ineligible for matching due to common support
+  restrictions.  It equals \texttt{TRUE} if unit $i$ was discarded,
+  and it is set to \texttt{FALSE} otherwise.
+  
+\item \texttt{distance}: a vector of length $n$ with the estimated
+  distance measure for each unit.
+  
+\item \texttt{weights}: a vector of length $n$ with the weights
+  assigned to each unit in the matching process.  Unmatched units have
+  weights equal to $0$. Matched treated units have weight $1$.  Each
+  matched control unit has weight proportional to the number of
+  treatment units to which it was matched, and the sum of the control
+  weights is equal to the number of uniquely matched control units.
+ 
+\item \texttt{subclass}: the subclass index in an ordinal scale from 1
+  to the total number of subclasses as specified in \texttt{subclass}
+  (or the total number of subclasses from full or exact matching).
+  Unmatched units have \texttt{NA}.
+  
+\item \texttt{q.cut}: the subclass cut-points that classify the
+  distance measure.
+  
+\item \texttt{treat}: the treatment indicator from \texttt{data} (the
+  left-hand side of \texttt{formula}).
+ 
+\item \texttt{X}: the covariates used for estimating the distance
+  measure (the right-hand side of \texttt{formula}).  When applicable,
+  \texttt{X} is augmented by covariates contained in \texttt{mahvars}
+  and \texttt{exact}.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+If you use \MatchIt, please cite
+\begin{verse}
+\bibentry{HoImaKin06}
+and 
+\bibentry{HoImaKin05b}
+\end{verse}
+
+The {\tt convex.hull} discard option is implemented via the {\tt
+  WhatIf} package.  If you use this option, please cite
+\begin{verse}
+\bibentry{StoKinZen05} 
+and either
+\bibentry{KinZen06a}
+or
+\bibentry{KinZen06b}
+\end{verse} 
+
+Generalized linear distance measures are implemented via the {\tt
+stats} package.  If you use this distance measure, please cite 
+\begin{verse}
+\bibentry{VenRip02}
+\end{verse}
+
+Generalized additive distance measures are implemented via the {\tt
+  mcgv} package.  If you use this distance measure, please cite
+\begin{verse}
+\bibentry{HasTib90}
+\end{verse}  
+
+The neural network distance measure is implemented via the {\tt nnet}
+package.  If you use this distance measure, please cite
+\begin{verse}
+\bibentry{Ripley96}
+\end{verse}
+
+The classification trees distance measure is implemented via the {\tt
+  rpart} package.  If you use this distance measure, please cite
+\begin{verse}
+\bibentry{BreFriOls84}
+\end{verse}  
+
+Full and optimal matching are implemented via the {\tt optmatch}
+package by Ben Hansen. If you use either of these methods, please cite
+\begin{verse}
+\bibentry{Hansen04}
+\end{verse} 
+
+Genetic matching is implemented via the {\tt Matching} package by
+Jasjeet Sekhon.  If you use this method, please cite
+\begin{verse}
+\bibentry{DiaSek05}
+\end{verse}
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "matchit"
+%%% End: 
diff --git a/inst/doc/commands/mi.tex b/inst/doc/commands/mi.tex
new file mode 100644
index 0000000..83f0c90
--- /dev/null
+++ b/inst/doc/commands/mi.tex
@@ -0,0 +1,36 @@
+\section{{\tt mi}: Create a list of multiply imputed data frames}
+\label{model.end}
+
+\subsubsection{Description}
+
+Use {\tt mi()} to create a list of multiply imputed data frames for
+use with {\tt zelig()}.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+mi(data1, data2, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt data1}:  the first multiply imputed data frame.
+\item {\tt data2}: the second multiply imputed data frame.
+\item {\tt \dots}: additional multiply imputed data frames.
+\end{itemize}
+
+\subsubsection{Output Values}
+A list of data frames with class {\tt "mi"}
+\subsubsection{See Also}
+\begin{itemize}
+\item See \Sref{mi.ex} to use {\tt mi()} with {\tt zelig()}.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/commands/model.end.aux b/inst/doc/commands/model.end.aux
new file mode 100644
index 0000000..3a3a0c9
--- /dev/null
+++ b/inst/doc/commands/model.end.aux
@@ -0,0 +1,41 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.2}{\tt  model.end}: Cleaning up after optimization}{303}{section.13.2}}
+\newlabel{model.end}{{13.2}{303}{{\tt model.end}: Cleaning up after optimization\relax }{section.13.2}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{303}{section*.388}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{303}{section*.389}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{303}{section*.390}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{303}{section*.391}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{303}{section*.392}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{303}{section*.393}}
+\@setckpt{commands/model.end}{
+\setcounter{page}{304}
+\setcounter{equation}{0}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
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+\setcounter{section}{2}
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+}
diff --git a/inst/doc/commands/model.end.tex b/inst/doc/commands/model.end.tex
new file mode 100644
index 0000000..3433a60
--- /dev/null
+++ b/inst/doc/commands/model.end.tex
@@ -0,0 +1,49 @@
+\section{{\tt model.end}: Cleaning up after optimization}
+\label{model.end}
+
+\subsubsection{Description}
+
+The {\tt model.end()} function creates a list of regression output from
+{\tt optim()} output.  The list includes coefficients (from the {\tt optim() 
+par} output), a variance-covariance matrix (from the {\tt optim()} Hessian output), 
+and any terms, contrasts, or xlevels (from the model frame).  Use  {\tt 
+model.end()} after calling {\tt optim()}, but before assigning a class to the 
+regression output.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+model.end(res, mf)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt res}: the output from {\tt optim()} or another fitting-algorithm.  
+\item {\tt mf}: the model frame output by {\tt model.frame()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+A list of regression output, including:
+\begin{itemize}
+\item {\tt coefficients}: the optimized parameters.
+\item {\tt variance}: the variance-covariance matrix (the negative
+  inverse of the Hessian matrix returned from the optimization
+  procedure).  
+\item {\tt terms}:  the terms object.  See {\tt help(terms.object)}
+  for more information.
+\item {\tt \dots}: additional elements passed from {\tt res}.
+\end{itemize}
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{s:new} for an overview of how to write a new model.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/commands/model.frame.multiple.aux b/inst/doc/commands/model.frame.multiple.aux
new file mode 100644
index 0000000..581e58f
--- /dev/null
+++ b/inst/doc/commands/model.frame.multiple.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.3}{\tt  model.frame.multiple}: Extracting the ``environment'' of a model formula}{304}{section.13.3}}
+\newlabel{model.frame.multiple}{{13.3}{304}{{\tt model.frame.multiple}: Extracting the ``environment'' of a model formula\relax }{section.13.3}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{304}{section*.394}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{304}{section*.395}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{304}{section*.396}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{304}{section*.397}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{304}{section*.398}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{305}{section*.399}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{305}{section*.400}}
+\@setckpt{commands/model.frame.multiple}{
+\setcounter{page}{306}
+\setcounter{equation}{0}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
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diff --git a/inst/doc/commands/model.frame.multiple.tex b/inst/doc/commands/model.frame.multiple.tex
new file mode 100644
index 0000000..b6016aa
--- /dev/null
+++ b/inst/doc/commands/model.frame.multiple.tex
@@ -0,0 +1,80 @@
+\section{{\tt model.frame.multiple}: Extracting the ``environment'' of
+a model formula}
+\label{model.frame.multiple}
+
+\subsubsection{Description}
+Use {\tt model.frame.multiple()} after {\tt parse.par()} to create a
+data frame of the unique variables identified in the formula (or list
+of formulas).  
+  
+\subsubsection{Syntax}
+\begin{verbatim}
+model.frame.multiple(formula, data, eqn = NULL, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+  \item {\tt formula}: a list of formulas of class {\tt "multiple"},
+returned from {\tt parse.par()}.  
+  \item {\tt data}: a data frame containing all the variables used in
+{\tt formula}.  
+\item {\tt eqn}: an optional character string or vector of character strings 
+specifying the equations for which you would like to extract variables.  Defaults 
+to {\tt NULL}, which pulls out all the variables for all equations in {\tt 
+formula}.   
+ \item {\tt \dots}: additional arguments passed to {\tt
+model.frame.default()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output is a data frame (with a terms attribute) containing all the
+unique explanatory and response variables identified in the list of
+formulas.  By default, missing ({\tt NA}) values are listwise deleted.
+
+If {\tt as.factor()} appears on the left-hand side, the response
+variables will be returned as an indicator (0/1) matrix with columns
+corresponding to the unique levels in the factor variable.  
+	
+If any formula contains a {\tt tag()}, {\tt model.frame.multiple()}
+will return the original variable in the data frame and use the {\tt
+tag()} information in the terms attribute only.
+
+\subsubsection{Examples}
+\begin{verbatim}
+formulae <- list(import ~ coop + cost + target,
+                 export ~ coop + cost + target)
+fml <- parse.formula(formulae, model = "bivariate.logit")
+D <- model.frame(fml, data = mydata)
+\end{verbatim}
+Since the output from {\tt parse.formula()} is of class {\tt
+"multiple"}, you do not need to call {\tt model.frame.multiple()}
+explicitly, but can use the generic {\tt model.frame()} instead.    
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{parse.formula} for {\tt parse.formula()}
+\item \Sref{ui} for an overview of the user-interface.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/commands/model.matrix.multiple.aux b/inst/doc/commands/model.matrix.multiple.aux
new file mode 100644
index 0000000..5567eb8
--- /dev/null
+++ b/inst/doc/commands/model.matrix.multiple.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.4}{\tt  model.matrix.multiple}: Design matrix for multivariate models}{306}{section.13.4}}
+\newlabel{model.matrix.multiple}{{13.4}{306}{{\tt model.matrix.multiple}: Design matrix for multivariate models\relax }{section.13.4}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{306}{section*.401}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{306}{section*.402}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{306}{section*.403}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{306}{section*.404}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{307}{section*.405}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{307}{section*.406}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{307}{section*.407}}
+\@setckpt{commands/model.matrix.multiple}{
+\setcounter{page}{308}
+\setcounter{equation}{0}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
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+}
diff --git a/inst/doc/commands/model.matrix.multiple.tex b/inst/doc/commands/model.matrix.multiple.tex
new file mode 100644
index 0000000..ec6b9d6
--- /dev/null
+++ b/inst/doc/commands/model.matrix.multiple.tex
@@ -0,0 +1,111 @@
+\section{{\tt model.matrix.multiple}: Design matrix for multivariate models}
+\label{model.matrix.multiple}
+
+\subsubsection{Description}
+Use {\tt model.matrix.multiple()} after {\tt parse.formula()} to
+create a design matrix for multiple-equation models.  
+  
+\subsubsection{Syntax}
+\begin{verbatim}
+model.matrix(object, data, shape = "compact", eqn = NULL, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+  \item {\tt object}: the list of formulas output from \texttt{parse.formula()}.
+  \item {\tt data}: a data frame created with {\tt model.frame.multiple()}. 
+ \item {\tt shape}: a character string specifying the shape of the
+ outputed matrix.  Available options are:  
+
+\begin{itemize} 
+
+\item {\tt "compact"}: (default) the output matrix will be an $n \times v$,
+where $v$ is the number of unique variables in all of the equations
+(including the intercept term). 
+
+\item {\tt "array"}: the output is an $n \times K \times J$ array where $J$ is the
+total number of equations and $K$ is the total number of parameters
+across all the equations.  If a variable is not in a certain equation,
+it is observed as a vector of 0s. 
+
+\item {\tt "stacked"}: the output will be a $2n \times K$ matrix where $K$ is the total number of parameters across all the equations.
+
+\end{itemize}
+  
+  \item {\tt eqn}: a character string or a vector of character
+  strings identifying the equations from which to construct the design
+  matrix. The defaults to {\tt NULL}, which only uses the systematic
+  parameters (for which {\tt DepVar = TRUE} in the appropriate {\tt
+describe.model()}) .  
+\item {\tt \dots}:  additional arguments passed to {\tt model.matrix.default()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+A design matrix or array, depending on the options chosen in {\tt
+shape}, with appropriate terms attributes.  
+
+\subsubsection{Examples}
+Let's say that the name of the model is {\tt "bivariate.probit"}, and
+the corresponding describe function is {\tt
+describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
+as systematic components, and an ancillary parameter {\tt rho}, which
+may be parameterized, but is estimated as a scalar by default.  Let
+{\tt par} be the parameter vector (including parameters for {\tt
+rho}), {\tt formulae} a user-specified formula given in one of the
+formats in Table \ref{good.formulas}, and {\tt mydata} the user
+specified data frame.  
+
+Acceptable combinations of {\tt parse.par()} and {\tt model.matrix()}
+are as follows:
+\begin{verbatim}
+## Setting up
+fml <- parse.formula(formulae, model = "bivariate.probit")
+D <- model.frame(fml, data = mydata)
+terms <- terms(D)
+
+## Intuitive option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
+eta <- X %*% Beta
+
+## Memory-efficient (compact) option (default)
+Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))    
+X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
+eta <- X %*% Beta
+
+## Computationally-efficient (array) option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
+eta <- apply(X, 3, '%*%', Beta)
+\end{verbatim}
+In each case, {\tt eta} is an $n \times 2$ matrix with columns
+corresponding to the linear predictors for {\tt mu1} and {\tt mu2},
+respectively.  
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{parse.par} for selecting and shaping parameter vectors.  
+\item \Sref{s:new} for examples of how to write new models.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/commands/names.tex b/inst/doc/commands/names.tex
new file mode 100644
index 0000000..2390432
--- /dev/null
+++ b/inst/doc/commands/names.tex
@@ -0,0 +1,57 @@
+\section{{\tt names}: Identifying the Structure of Zelig Objects}
+
+\subsubsection{Description}
+  The function \texttt{names()} returns the named elements within an
+  object.  For objects created using \texttt{zelig()} and
+  \texttt{sim()}, \texttt{names()} returns nested lists as
+  object-level arguments.  Use the \texttt{\$} operator to extract 
+elements
+  from objects.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> names(x)                                            # Lists elements. 
+> names(x) <- "value"                                 # Assigns names to x.  
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt x}: any R object. 
+\item {\tt "value"}: a character vector of up to the same length as
+  \texttt{x} (or \texttt{NULL} to clear the names from the object).
+\end{itemize}
+
+\subsubsection{Output Values}
+For the generic function \texttt{names}, \texttt{NULL} or a character
+vector of the same length as \texttt{x}.
+
+\subsubsection{Example}
+Attach sample data:  
+\begin{verbatim}
+> data(turnout)
+\end{verbatim}
+Return the variables in the data frame:  
+\begin{verbatim}
+> names(turnout)
+\end{verbatim}
+Generate Zelig objects:
+\begin{verbatim}
+> z.out <- zelig(vote ~ age + income, model = "logit", data = turnout)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Extract names from objects of class zelig:
+\begin{verbatim}
+> names(z.out)
+> names(s.out)
+> names(summary(s.out))
+\end{verbatim}
+
+\subsubsection{See Also}
+
+Additional information is available in {\tt help(names)}.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/commands/parse.formula.aux b/inst/doc/commands/parse.formula.aux
new file mode 100644
index 0000000..c02cfe3
--- /dev/null
+++ b/inst/doc/commands/parse.formula.aux
@@ -0,0 +1,44 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.5}{\tt  parse.formula}: Parsing the inputs}{308}{section.13.5}}
+\newlabel{parse.formula}{{13.5}{308}{{\tt parse.formula}: Parsing the inputs\relax }{section.13.5}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{308}{section*.408}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{308}{section*.409}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{308}{section*.410}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{308}{section*.411}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{308}{section*.412}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{308}{section*.413}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{309}{section*.414}}
+\@writefile{lot}{\contentsline {table}{\numberline {13.1}{\ignorespaces Examples of acceptable short-hand for user-specified formulas, using bivariate probit as an example}}{310}{table.13.1}}
+\newlabel{good.formulas}{{13.1}{310}{Contributors\relax }{table.13.1}{}}
+\@setckpt{commands/parse.formula}{
+\setcounter{page}{311}
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diff --git a/inst/doc/commands/parse.formula.tex b/inst/doc/commands/parse.formula.tex
new file mode 100644
index 0000000..968b1ad
--- /dev/null
+++ b/inst/doc/commands/parse.formula.tex
@@ -0,0 +1,113 @@
+\section{{\tt parse.formula}: Parsing the inputs}
+\label{parse.formula}
+
+\subsubsection{Description}
+Parse the input formula (or list of formulas) into the standard format
+described below.  Since labels for this format will vary by model,
+{\tt parse.formula()} will evaluate a function {\tt describe.model()},
+where {\tt model} is given as an input to {\tt parse.formula()}.
+
+If the {\tt check.model()} function has more than one parameter for
+which {\tt ExpVar = TRUE} and {\tt DepVar = TRUE}, then the
+user-specified equations must have labels to match those parameters,
+else {\tt parse.formula()} should return an error. In addition, if the
+formula entries are not unambiguous, then {\tt parse.formula()} should
+return an error.
+  
+\subsubsection{Syntax}
+\begin{verbatim}
+> fml <- parse.formula(formula, model, data = NULL)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+  \item {\tt formula}: either a single formula or a list of formula
+objects.  
+  \item {\tt model}: a character string specifying the name of the model.
+  \item {\tt data}: an optional data frame for models that require a
+factor response variable.
+\end{itemize}
+
+\subsubsection{Output Values}
+The output is a list of formula objects with class {\tt ("multiple",
+"list")}.  Let's say that the name of the model is {\tt
+"bivariate.probit"}, and the corresponding describe function is {\tt
+describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
+as systematic components, and an ancillary parameter {\tt rho}, which
+may be parameterized, but is estimated as a scalar by default.  Given
+this model, Table \ref{good.formulas} gives acceptable user inputs.  
+
+\subsubsection{Examples}
+\begin{verbatim}
+formulae <- list(cbind(import, export) ~ coop + cost + target)
+fml <- parse.formula(formulae, model = "bivariate.probit")
+D <- model.frame(fml, data = mydata)
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item \Sref{ui} for commented examples of how {\tt parse.formula()}
+and {\tt describe.model()} work together.  
+  \item \Sref{tag} for constraints between coefficients in a multiple
+equation context.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+\clearpage
+\begin{landscape}
+\begin{table}[h!]
+\caption{Examples of acceptable short-hand for user-specified
+formulas, using bivariate probit as an example}
+\label{good.formulas}
+\begin{center}
+\begin{tabular}{lll}
+\\
+ & User Input    & Output from {\tt parse.formula()} \\
+\hline
+\\
+Same covariates, & \verb|cbind(y1, y2) ~ x1 + x2 * x3|  & \verb|list(mu1 = y1 ~ x1 + x2 * x3,|\\
+separate effects & & \verb|     mu2 = y2 ~ x1 + x2 * x3,| \\
+& &                  \verb|     rho = ~ 1)| \\
+\\
+With $\rho$ as a & \verb|list(cbind(y1, y2) ~ x1 + x2,| &
+\verb|list(mu1 = y1 ~ x1 + x2,|\\
+systematic equation & \verb|    rho = ~ x4 + x5)| & \verb|     mu2 = y2 ~ x1 + x2,|\\
+& & \verb|     rho = ~ x4 + x5)| \\
+\\
+With constraints & \verb|list(mu1 = y1 ~ x1 + tag(x2, "x2"),| &
+\verb|list(mu1 = y1 ~ x1 + tag(x2, "x2"),| \\ 
+(same variable)  & \verb|     mu2 = y2 ~ x3 + tag(x2, "x2"))| &
+\verb|     mu2 = y2 ~ x3 + tag(x2, "x2"),|\\
+& & \verb|     rho = ~ 1)|\\
+\\
+With constraints & \verb|list(mu1 = y1 ~ x1 + tag(x2, "z1"),| &
+\verb|list(mu1 = y1 ~ x1 + tag(x2, "z1"),| \\ 
+(different variables)  & \verb|     mu2 = y2 ~ x3 + tag(x4, "z1"))| &
+\verb|     mu2 = y2 ~ x3 + tag(x4, "z1"),|\\
+& & \verb|     rho = ~ 1)|\\
+\end{tabular}
+\end{center}
+\end{table}
+\end{landscape}
+
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/commands/parse.par.aux b/inst/doc/commands/parse.par.aux
new file mode 100644
index 0000000..d8bbc09
--- /dev/null
+++ b/inst/doc/commands/parse.par.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.6}{\tt  parse.par}: Select and reshape parameter vectors}{311}{section.13.6}}
+\newlabel{parse.par}{{13.6}{311}{{\tt parse.par}: Select and reshape parameter vectors\relax }{section.13.6}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{311}{section*.415}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{311}{section*.416}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{311}{section*.417}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{311}{section*.418}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{311}{section*.419}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{312}{section*.420}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{312}{section*.421}}
+\@setckpt{commands/parse.par}{
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diff --git a/inst/doc/commands/parse.par.tex b/inst/doc/commands/parse.par.tex
new file mode 100644
index 0000000..a806b9d
--- /dev/null
+++ b/inst/doc/commands/parse.par.tex
@@ -0,0 +1,95 @@
+\section{{\tt parse.par}: Select and reshape parameter vectors}\label{parse.par}
+
+\subsubsection{Description}
+
+The {\tt parse.par()} function reshapes parameter vectors for
+compatability with the output matrix from {\tt
+model.matrix.multiple()}. (See\Sref{model.matrix.multiple}.)  Use {\tt
+parse.par()} to identify sets of parameters; for example, within
+optimization functions that require vector input, or within {\tt qi()}
+functions that take matrix input of all parameters as a lump.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+parse.par(par, terms, shape = "matrix", eqn = NULL)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt par}: the vector (or matrix) of parameters.
+\item {\tt terms}: the terms from either {\tt model.frame.*()} or 
+		{\tt model.matrix.*()}.
+\item {\tt shape}: a character string (either {\tt "matrix"} or {\tt "vector"})
+that identifies the type of output structure. 
+\item {\tt eqn}: a character string (or strings) that identify the
+parameters that you would like to subset from the larger {\tt par}
+structure.
+\end{itemize}
+
+\subsubsection{Output Values}
+A matrix or vector of the sub-setted (and reshaped) parameters for the specified
+parameters given in {\tt eqn}.   By default, {\tt eqn = NULL}, such that all systematic
+components are selected.  (Systematic components have {\tt ExpVar =
+TRUE} in the appropriate {\tt describe.model()} function.)  
+
+If an ancillary parameter (for which {\tt ExpVar = FALSE} in
+{\tt describe.model()}) is specified in {\tt eqn}, it is
+always returned as a vector (ignoring {\tt shape}).  (Ancillary
+parameters are all parameters that have intercept only formulas.)  
+
+\subsubsection{Examples}
+Let's say that the name of the model is {\tt "bivariate.probit"}, and
+the corresponding describe function is {\tt
+describe.bivariate.probit()}, which identifies {\tt mu1} and {\tt mu2}
+as systematic components, and an ancillary parameter {\tt rho}, which
+may be parameterized, but is estimated as a scalar by default.  Let
+{\tt par} be the parameter vector (including parameters for {\tt
+rho}), {\tt formulae} a user-specified formula given in one of the
+formats in Table \ref{good.formulas}, and {\tt mydata} the user
+specified data frame.  
+
+Acceptable combinations of {\tt parse.par()} and {\tt model.matrix()}
+are as follows:
+\begin{verbatim}
+## Setting up
+fml <- parse.formula(formulae, model = "bivariate.probit")
+D <- model.frame(fml, data = mydata)
+terms <- terms(D)
+
+## Intuitive option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
+eta <- X %*% Beta
+
+## Memory-efficient (compact) option (default)
+Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))    
+X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
+eta <- X %*% Beta
+
+## Computationally-efficient (array) option
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
+eta <- apply(X, 3, '%*%', Beta)
+\end{verbatim}
+In each case, {\tt eta} is an $n \times 2$ matrix with columns
+corresponding to the linear predictors for {\tt mu1} and {\tt mu2},
+respectively.  
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{model.matrix.multiple} for a description of how multiple
+equation models work with {\tt model.matrix()}.  
+\item \Sref{constraints} for more detail on the three combinations
+(intuitive, memory-efficient, and computationally-efficient) methods
+of multiplying matrices of parameters and variables.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/commands/plot.ci.aux b/inst/doc/commands/plot.ci.aux
new file mode 100644
index 0000000..fadb11e
--- /dev/null
+++ b/inst/doc/commands/plot.ci.aux
@@ -0,0 +1,43 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {11.3}{\tt  plot.ci}: Plotting Vertical confidence Intervals}{114}{section.11.3}}
+\newlabel{plot.ci}{{11.3}{114}{{\tt plot.ci}: Plotting Vertical confidence Intervals\relax }{section.11.3}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{114}{section*.101}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{114}{section*.102}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{114}{section*.103}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{114}{section*.104}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{114}{section*.105}}
+\citation{KinTomWit00}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{115}{section*.106}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{115}{section*.107}}
+\@setckpt{commands/plot.ci}{
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+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/commands/plot.ci.tex b/inst/doc/commands/plot.ci.tex
new file mode 100644
index 0000000..99ebfd0
--- /dev/null
+++ b/inst/doc/commands/plot.ci.tex
@@ -0,0 +1,86 @@
+\section{{\tt plot.ci}: Plotting Vertical confidence Intervals}\label{plot.ci}
+
+\subsubsection{Description}
+The \texttt{plot.ci()} command generates vertical confidence intervals
+for linear or generalized linear univariate response models.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> plot.ci(object, CI = 95, qi = "ev", col = c("red", "blue"), ...) 
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt object}: stored output from {\tt sim()}.  The {\tt x} and optional
+  {\tt x1} values used to generate the {\tt sim()} output object must
+  have more than one observation.  
+\item {\tt CI}: the selected confidence interval.  Defaults to 95
+  percent.  
+\item {\tt qi}: the selected quantity of interest.  Defaults to
+  expected values.
+\item {\tt col}: a vector of at most two colors for plotting the
+  expected value given by {\tt x} and the alternative set of expected
+  values given by {\tt x1} in {\tt sim()}.  If the quantity of
+  interest selected is not the expected value, or {\tt x1 = NULL},
+  only the first color will be used. 
+\item {\tt \dots}: Additional parameters, such as {\tt xlab}, {\tt
+    ylab}, and {\tt main}, passed to {\tt plot()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+For all univariate response models, {\tt plot.ci()} returns vertical
+confidence intervals over a specified range of one explanatory
+variable.  You may save this plot using the commands described in
+\Sref{ss:output}.  
+
+\subsubsection{Example}
+
+This demo is available within R as {\tt demo(vertci)}.  Load the
+sample data and estimate the model.
+\begin{verbatim}
+data(turnout)
+z.out <- zelig(vote ~ race + educate + age + I(age^2) + income,
+               model = "logit", data = turnout)
+\end{verbatim}
+Establish a range for {\tt `age'}, the key explanatory variable, and
+create two {\tt setx} objects that hold the other explanatory
+variables to fixed, while allowing age to range from 18 to 95, inclusive.
+\begin{verbatim}
+age.range <- 18:95
+x.low <- setx(z.out, educate = 12, age = age.range)
+x.high <- setx(z.out, educate = 16, age = age.range)
+\end{verbatim}
+Simulate quantities of interest.  
+\begin{verbatim}
+s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{verbatim}
+Plot these results using the {\tt plot.ci()} function:  
+\begin{verbatim}
+plot.ci(s.out, xlab = "Age in Years",
+        ylab = "Predicted Probability of Voting",
+        main = "Effect of Education and Age on Voting Behavior")
+legend(45, 0.52, legend = c("College Education (16 years)",
+       "High School Education (12 years)"), col = c("blue","red"), 
+       lty = c("solid"))
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item The {\tt help(plot)} and {\tt help(lines)} reference pages.    
+  \item \Sref{s:plot} for an overview of plotting procedures
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau created the R procedure to
+generate vertical confidence intervals for simulated quantities of
+interest.  
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.  
+\end{verse}
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/commands/plot.zelig.aux b/inst/doc/commands/plot.zelig.aux
new file mode 100644
index 0000000..713fb8f
--- /dev/null
+++ b/inst/doc/commands/plot.zelig.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {10.5}{\tt  plot}: Graphing Quantities of Interest}{101}{section.10.5}}
+\newlabel{ss:plot.zelig}{{10.5}{101}{{\tt plot}: Graphing Quantities of Interest\relax }{section.10.5}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{101}{section*.63}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{101}{section*.64}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{101}{section*.65}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{101}{section*.66}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{101}{section*.67}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{102}{section*.68}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{102}{section*.69}}
+\@setckpt{commands/plot.zelig}{
+\setcounter{page}{103}
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diff --git a/inst/doc/commands/plot.zelig.tex b/inst/doc/commands/plot.zelig.tex
new file mode 100644
index 0000000..90d7ff5
--- /dev/null
+++ b/inst/doc/commands/plot.zelig.tex
@@ -0,0 +1,68 @@
+\section{{\tt plot}: Graphing Quantities of Interest}\label{ss:plot.zelig}
+
+\subsubsection{Description}
+The \texttt{plot()} command generates default plots for {\tt
+  sim()} objects with one-observation values in {\tt x} and {\tt x1}.
+  Additional default plotting methods for other data structures are
+  described in {\tt help(plot)}.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> plot(s.out, alt.col = "red", user.par = FALSE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item{\tt s.out}: stored output from {\tt sim()}.  If the {\tt x} or
+  {\tt x1} values stored in the object contain more than one
+  observation, {\tt plot.zelig()} will return an error.  For linear or
+  generalized linear models with more than one observation in {\tt x}
+  and optionally {\tt x1}, you may use {\tt plot.ci()} (see \Sref{plot.ci}).
+\item{\tt alt.col}: alternative color used for contrast in the plots.
+  The primary color is black.  Type {\tt colors()} to see a list of
+  available alternative colors.
+\item{\tt user.par}: a logical value indicating whether to use the
+  default Zelig plotting parameters ({\tt user.par = FALSE}) or
+  user-defined parameters ({\tt user.par = TRUE}), set using the {\tt
+    par()} function prior to plotting.
+\item{\tt \dots}: Additional parameters passed to {\tt plot()}.
+  Because {\tt plot.zelig()} primarily produces diagnostic plots, many
+  of these parameters are hard-coded for convenience and
+  presentation.  If you wish to produce plots using different
+  parameters, you may follow the directions in \Sref{s:plot}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+Depending on the class of model selected, \texttt{plot.zelig()} will
+return an on-screen window with graphs of the various quantities of
+interest.  You may save these plots using the commands described in
+\Sref{ss:output}.  
+
+\subsubsection{Examples}
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, model = "logit", data = mydata)
+> x.out <- setx(z.out, x1 = 10)
+> x.alt <- setx(z.out, x1 = 0)
+> s.out <- sim(z.out, x = x.out, x1 = x.alt)
+> plot(s.out)
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+\item The {\tt help(plot)} and {\tt help(lines)} reference pages for
+  optional arguments passed to {\tt \dots}.
+\item The {\tt help(par)} reference page for user-specified plotting
+  parameters.     
+\item \Sref{s:plot} for an overview of plotting procedures
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau created the plots generated by
+{\tt plot.zelig()}.  These plots are individually available in the
+base, MASS, and vcd libraries.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/commands/print.aux b/inst/doc/commands/print.aux
new file mode 100644
index 0000000..62e8589
--- /dev/null
+++ b/inst/doc/commands/print.aux
@@ -0,0 +1,41 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {10.6}{\tt  print}: Printing Quantities of Interest}{103}{section.10.6}}
+\newlabel{ss:print}{{10.6}{103}{{\tt print}: Printing Quantities of Interest\relax }{section.10.6}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{103}{section*.70}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{103}{section*.71}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{103}{section*.72}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{103}{section*.73}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{103}{section*.74}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{103}{section*.75}}
+\@setckpt{commands/print}{
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+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
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diff --git a/inst/doc/commands/print.tex b/inst/doc/commands/print.tex
new file mode 100644
index 0000000..edd4134
--- /dev/null
+++ b/inst/doc/commands/print.tex
@@ -0,0 +1,51 @@
+\section{{\tt print}: Printing Quantities of Interest}
+\label{ss:print}
+
+\subsubsection{Description}
+The {\tt print} command formats summary output and allows users to
+specify the number of decimal places as an optional argument.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> print(x, digits = 3, print.x = FALSE)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt x}: the object to be printed may be {\tt z.out} output
+  from {\tt zelig()}, {\tt x.out} output from {\tt setx()}, {\tt s.out}
+  output from {\tt sim()}, or other R data structures.
+\item {\tt digits}: the minimum number of significant digits to return
+  for all elements of $x < 0$.  By default, {\tt print()} avoids
+  scientific notation, but setting the number of digits to 1 will
+  frequently force output in scientific notation.  The number of {\tt
+    digits} is not the number of significant digits for all output
+  values, but the minimum number of significant digits for the
+  smallest value in {\tt x} between -1 and 1; this governs the number
+  of significant digits in the rest of the values with decimal output.
+\item {\tt print.x}: a logical value for {\tt sim()} output, which
+  specifies whether to print a summary ({\tt print.x = FALSE}, the
+  default) of the {\tt x} and {\tt x1} \emph{inputs} to {\tt sim()},
+  or the complete set of inputs (optionally, {\tt print.x = TRUE}).
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{verbatim}
+> print(summary(z.out), digits = 2)
+> print(summary(s.out), digits = 3, print.x = TRUE)
+\end{verbatim}
+
+\subsubsection{See Also}
+
+Advanced users may wish to refer to {\tt help(print)}.
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau added print methods for {\tt
+  sim()} output, and {\tt summary()} output for Zelig objects.   
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/commands/put.start.aux b/inst/doc/commands/put.start.aux
new file mode 100644
index 0000000..54ace9a
--- /dev/null
+++ b/inst/doc/commands/put.start.aux
@@ -0,0 +1,41 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.7}{\tt  put.start}: Set specific starting values for certain parameters}{313}{section.13.7}}
+\newlabel{put.start}{{13.7}{313}{{\tt put.start}: Set specific starting values for certain parameters\relax }{section.13.7}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{313}{section*.422}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{313}{section*.423}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{313}{section*.424}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{313}{section*.425}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{313}{section*.426}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{313}{section*.427}}
+\@setckpt{commands/put.start}{
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diff --git a/inst/doc/commands/put.start.tex b/inst/doc/commands/put.start.tex
new file mode 100644
index 0000000..1d7d9af
--- /dev/null
+++ b/inst/doc/commands/put.start.tex
@@ -0,0 +1,44 @@
+\section{{\tt put.start}: Set specific starting values for certain parameters}
+\label{put.start}
+
+\subsubsection{Description}
+
+After calling {\tt set.start()} to create default starting values, use
+{\tt put.start()} to change starting values for specific parameters or
+parameter sets. 
+
+\subsubsection{Syntax}
+\begin{verbatim}
+put.start(start.val, value, terms, eqn)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt start.val}: the vector of starting values created by {\tt
+set.start()}. 
+\item {\tt value}: the scalar or vector of replacement starting
+values.  
+\item {\tt terms}: the terms output from {\tt model.frame.multiple()}.
+\item {\tt eqn}: the parameters for which you would like to replace
+the default values with {\tt value}.    
+\end{itemize}
+
+\subsubsection{Output Values}
+A vector of starting values (of the same length as {\tt start.val}).  
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{set.start} to set default starting values.  
+\item \Sref{s:new} for an overview of the procedure to add
+  models to Zelig.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/commands/replicate.aux b/inst/doc/commands/replicate.aux
new file mode 100644
index 0000000..4445cb0
--- /dev/null
+++ b/inst/doc/commands/replicate.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {10.7}{\tt  repl}: Replicating Analyses}{104}{section.10.7}}
+\newlabel{ss:replicate}{{10.7}{104}{{\tt repl}: Replicating Analyses\relax }{section.10.7}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{104}{section*.76}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{104}{section*.77}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{104}{section*.78}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{104}{section*.79}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{105}{section*.80}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{105}{section*.81}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{105}{section*.82}}
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diff --git a/inst/doc/commands/replicate.tex b/inst/doc/commands/replicate.tex
new file mode 100644
index 0000000..ce7723a
--- /dev/null
+++ b/inst/doc/commands/replicate.tex
@@ -0,0 +1,82 @@
+\section{{\tt repl}: Replicating Analyses}\label{ss:replicate}
+
+\subsubsection{Description}
+The {\tt repl()} command included with Zelig takes {\tt zelig()}
+or {\tt sim()} output objects and replicates (literally, re-runs) the
+entire analysis.  The results should be an output object identical to
+the original input object in the case of {\tt zelig()} output.  In the
+case of {\tt sim()} output, the replicated analyses may differ
+slightly due to stochastic randomness in the simulation procedure.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> repl(z.out, data = NULL)
+> repl(s.out, prev = NULL, data = NULL, x = NULL, x1 = NULL,
+       bootfn = NULL) 
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt z.out}: Stored output from {\tt zelig()}.  
+\item {\tt s.out}: Stored output from {\tt sim()}
+\item {\tt data}: You may manually input the data frame name rather
+  than allowing replicate to draw the data frame name from the object
+  to be replicated.  
+\item {\tt prev}: When replicating {\tt sim()} output, you may
+  optionally use the previously simulated parameters to calculate the
+  quantities of interest rather than simulating a new set of
+  parameters.  For all models, this should produce identical
+  quantities of interest.  If the parameters were
+  bootstrapped in the original analysis, this will save a considerable
+  amount of time.
+\item {\tt x}: When replicating {\tt sim()} output, you may
+  optionally use an alternative {\tt setx()} value for the {\tt x}
+  input.  
+\item {\tt x1}: When replicating {\tt sim()} output, you may
+  optionally use an alternative {\tt setx()} object for the {\tt x1}
+  input to replicating the {\tt sim()} object.
+\item {\tt bootfn}: When replicating {\tt sim()} output with
+  bootstrapped parameters, you should manually specify the {\tt
+    bootfn} if a non-default option was used.  
+\end{itemize}
+
+\subsubsection{Output Values}
+For {\tt zelig()} output, {\tt repl()} will create output that is in
+every way identical to the original input.  You may check to see
+whether they are identical by using the {\tt identical()} command.  
+
+For {\tt sim()} output, {\tt repl()} output will be will be
+identical to the original object if you choose not to simulate new
+parameters, and instead choose to calculate quantities of interest
+using the previously simulated parameters (using the {\tt prev}
+option.  If you choose to simulate new parameters, the summary
+statistics for each quantity of interest should be identical, up to a
+random approximation error.  As the number of simulations increases,
+this error decreases.
+
+\subsubsection{Examples}
+\begin{verbatim}
+> data(turnout)
+> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout[1:1000,])
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+> z.rep <- repl(z.out)
+> identical(z.out$coef, z.rep$coef)
+> z.alt <- repl(z.out, data = turnout[1001:2000,])
+> s.rep <- repl(s.out, prev = s.out$par)
+> identical(s.out$ev, s.rep$ev)
+\end{verbatim} %$
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item \Sref{load.data} for loading data from disk.  
+\end{itemize} 
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/commands/rocplot.aux b/inst/doc/commands/rocplot.aux
new file mode 100644
index 0000000..fe81f38
--- /dev/null
+++ b/inst/doc/commands/rocplot.aux
@@ -0,0 +1,43 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {11.4}{\tt  rocplot}: Receiver Operator Characteristic Plots}{116}{section.11.4}}
+\newlabel{ss:rocplot}{{11.4}{116}{{\tt rocplot}: Receiver Operator Characteristic Plots\relax }{section.11.4}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{116}{section*.108}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{116}{section*.109}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{116}{section*.110}}
+\citation{KinTomWit00}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{117}{section*.111}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{117}{section*.112}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{117}{section*.113}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{117}{section*.114}}
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diff --git a/inst/doc/commands/rocplot.tex b/inst/doc/commands/rocplot.tex
new file mode 100644
index 0000000..b21ab71
--- /dev/null
+++ b/inst/doc/commands/rocplot.tex
@@ -0,0 +1,91 @@
+\section{{\tt rocplot}: Receiver Operator Characteristic Plots}\label{ss:rocplot}
+
+\subsubsection{Description}
+The \texttt{rocplot()} command generates a receiver operator
+characteristic plot to compare the in-sample (default) or
+out-of-sample fit for two logit or probit regressions.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+rocplot(y1, y2, fitted1, fitted2, cutoff = seq(from=0, to=1, length=100), 
+        lty1 = "solid", lty2 = "dashed", lwd1 = par("lwd"), lwd2 = par("lwd"),
+        col1 = par("col"), col2 = par("col"), plot = TRUE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt y1}: Response variable for the first model.  
+\item {\tt y2}: Response variable for the second model. 
+\item {\tt fitted1}: Fitted values for the first model.  These values
+  may represent either the in-sample or out-of-sample fitted values.
+\item {\tt fitted2}: Fitted values for the second model.  
+\item {\tt cutoff}: A vector of cut-off values between 0 and 1, at
+  which to evaluate the proportion of 0s and 1s correctly predicted by
+  the first and second model.  By default, this is 100 increments
+  between 0 and 1, inclusive.  
+\item {\tt lty1}, {\tt lty2}: The line type for the first model ({\tt
+    lty1}) and the second model ({\tt lty2}), defaulting to solid and
+  dashed, respectively.    
+\item {\tt lwd1}, {\tt lwd2}: The width of the line for the first
+  model ({\tt lwd1}) and the second model ({\tt lwd2}), defaulting to
+  1 for both.  
+\item {\tt col1}, {\tt col2}: The colors of the line for the first
+  model ({\tt col1}) and the second model ({\tt col2}), defaulting to
+  black for both.
+\item{\tt plot}: By default, {\tt plot = TRUE}, which generates a plot to the
+  selected device.  If {\tt FALSE}, {\tt rocplot()} returns a list of output (see below).  
+\item {\tt \dots}: Additional parameters passed to plot, including
+  {\tt xlab}, {\tt ylab}, and {\tt main}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+
+If {\tt plot = TRUE}, {\tt rocplot()} generates an ROC plot for two
+logit or probit models.  You may save this plot using the commands
+described in \Sref{ss:output}.
+
+If {\tt plot = FALSE}, {\tt rocplot()} returns a list with the following
+elements:
+\begin{itemize}
+\item {\tt roc1}:  A matrix containing a vector of x-coordinates and
+  y-coordinates corresponding to the number of ones and zeros correctly
+  predicted for the first model.
+\item {\tt roc2}:  A matrix containing a vector of x-coordinates and
+  y-coordinates corresponding to the number of ones and zeros correctly
+  predicted for the second model.
+\item {\tt area1}:  The area under the first ROC curve, calculated using
+  Reimann sums.
+\item {\tt area2}:  The area under the second ROC curve, calculated using
+  Reimann sums.
+\end{itemize}
+
+\subsubsection{Example}
+You may view this example using {\tt demo(roc)}.  
+\begin{verbatim}
+> data(turnout)
+> z.out1 <- zelig(vote ~ race + educate + age, model = "logit", 
+                  data = turnout)
+> z.out2 <- zelig(vote ~ race + educate, model = "logit", 
+                  data = turnout)
+> rocplot(z.out1$y, z.out2$y, fitted(z.out1), fitted(z.out2))
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item The {\tt help(plot)} and {\tt help(lines)} reference pages.  
+  \item \Sref{s:plot} for an overview of plotting procedures.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau.  
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.  
+\end{verse}
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
diff --git a/inst/doc/commands/set.start.aux b/inst/doc/commands/set.start.aux
new file mode 100644
index 0000000..b3d7a3c
--- /dev/null
+++ b/inst/doc/commands/set.start.aux
@@ -0,0 +1,42 @@
+\relax 
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+\newlabel{set.start}{{13.8}{314}{{\tt set.start}: Set starting values for all parameters\relax }{section.13.8}{}}
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+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{314}{section*.429}}
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+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{314}{section*.431}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{314}{section*.432}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{314}{section*.433}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{314}{section*.434}}
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diff --git a/inst/doc/commands/set.start.tex b/inst/doc/commands/set.start.tex
new file mode 100644
index 0000000..70c68f9
--- /dev/null
+++ b/inst/doc/commands/set.start.tex
@@ -0,0 +1,53 @@
+\section{{\tt set.start}: Set starting values for all parameters}
+\label{set.start}
+
+\subsubsection{Description}
+
+After using {\tt parse.par()} and {\tt model.matrix()}, use {\tt
+set.start()} to set starting values for all parameters.  By default,
+starting values are set to 0.  If you wish to select alternative
+starting values for certain parameters, use {\tt put.start()} after
+{\tt set.start()}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+set.start(start.val = NULL, terms)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+\begin{itemize}
+\item {\tt start.val}: user-specified starting values.  If {\tt NULL}
+(default), the default starting values for all parameters are set to
+0.  
+\item {\tt terms}: the terms output from {\tt
+model.frame.multiple()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+A named vector of starting values for all parameters specified in {\tt
+terms}, defaulting to 0.  
+
+\subsubsection{Example}
+\begin{verbatim}
+fml <- parse.formula(formula, model = "bivariate.probit")
+D <- model.frame(fml, data = data)
+terms <- terms(D)
+start.val <- set.start(start.val = NULL, terms)
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{put.start} to change starting values for specific
+parameter sets.  
+\item \Sref{s:new} for detailed examples of writing new models.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau and Ferdinand Alimadhi.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/commands/setx.aux b/inst/doc/commands/setx.aux
new file mode 100644
index 0000000..07a0fe2
--- /dev/null
+++ b/inst/doc/commands/setx.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {10.2}{\tt  setx}: Setting Explanatory Variable Values}{92}{section.10.2}}
+\newlabel{ss:setx}{{10.2}{92}{{\tt setx}: Setting Explanatory Variable Values\relax }{section.10.2}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{92}{section*.40}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{92}{section*.41}}
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+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{93}{section*.43}}
+\@writefile{toc}{\contentsline {subsubsection}{Example: Unconditional Prediction}{93}{section*.44}}
+\@writefile{toc}{\contentsline {subsubsection}{Example: Conditional Prediction With MatchIt Data}{93}{section*.45}}
+\citation{KinTomWit00}
+\citation{KinTomWit00}
+\@writefile{toc}{\contentsline {subsubsection}{Example: Conditional Prediction With Multiple Analyses}{94}{section*.46}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{94}{section*.47}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{94}{section*.48}}
+\@setckpt{commands/setx}{
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diff --git a/inst/doc/commands/setx.tex b/inst/doc/commands/setx.tex
new file mode 100644
index 0000000..9b066ef
--- /dev/null
+++ b/inst/doc/commands/setx.tex
@@ -0,0 +1,149 @@
+\section{{\tt setx}: Setting Explanatory Variable Values}\label{ss:setx}
+
+\subsubsection{Description}
+The \texttt{setx()} command uses the variables identified in the
+\texttt{formula} generated by \texttt{zelig()} and sets the values of
+the explanatory variables to the selected values.  Use \texttt{setx()}
+after {\tt zelig()} and before \texttt{sim()} to simulate quantities
+of interest.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> x.out <- setx(z.out, fn = list(numeric = mean, ordered = median, 
+                other = mode), data = NULL, cond = FALSE, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+  
+\item {\tt z.out}: the saved output from \texttt{zelig()}.
+  
+\item {\tt fn}: a list of three functions to apply to three types of
+  variables
+  \begin{itemize}
+  \item {\tt numeric} variables are set to their mean by default, but
+    you may select any other mathematical function.
+  \item {\tt ordered} factors are set to their median by default, and
+    most mathematical operations will work on them.  (If you select
+    {\tt ordered = mean}, however, {\tt setx()} will default to median
+    with a warning.)
+  \item {\tt other} variables may consist of unordered factors,
+    character strings, or logical variables.  The {\tt other}
+    variables may only be set to their mode.  (If you wish to set one
+    of the other variables to a specific value, you may do so using
+    {\tt \dots} or by making them numeric and using \texttt{fn}.)
+  \end{itemize}
+  In the special case {\tt fn = NULL}, {\tt setx()} will return all of
+  the observations without applying any function to the data.
+\item {\tt data}: a new data frame used to set the values of
+  explanatory variables. If {\tt data = NULL} (the default), the data
+  frame called in \texttt{zelig()} is used.
+\item {\tt cond}: a logical value indicating whether the usual
+  unconditional prediction is used (the default) or whether the optional
+  conditional prediction should be performed (choose {\tt cond =
+    TRUE}).  If you choose {\tt cond = TRUE}, {\tt setx()} will coerce
+  {\tt fn = NULL} and ignore the arguments in {\tt \dots}.
+\item {\tt \dots}: user-defined values of specific variables that
+  overwrite the default values set by the function.  For example,
+  adding {\tt var1 = mean(data\$var1)} sets the value of \texttt{var1}
+  to the mean of {\tt data\$var1}; {\tt x1 = 12} explicitly sets the value
+  of {\tt x1} to 12.  In addition, you may specify one explanatory
+  variable as a range of values, creating one observation for every
+  unique value in the range of values.  The other variables are set to
+  the values given by {\tt fn}.
+\end{itemize}
+
+\subsubsection{Output Values}
+\begin{itemize}
+\item For unconditional prediction, {\tt x.out} is a model matrix
+  based on the specified values for the explanatory variables.  For
+  multiple analyses (i.e., when choosing the {\tt by} option in {\tt
+    zelig()}, {\tt setx()} returns the selected values calculated over
+  the entire data frame.  If you wish to calculate values over just
+  one subset of the data frame, the 5th subset for example, you may
+  use:
+\begin{verbatim}
+> x.out <- setx(z.out[[5]])
+\end{verbatim}
+  
+\item For conditional prediction, {\tt x.out} includes the model matrix
+  and the dependent variable(s).  For multiple analyses (when choosing
+  the {\tt by} option in {\tt zelig()}), {\tt setx()} returns the
+  observed explanatory variables in each subset.
+\end{itemize}
+
+\subsubsection{Example: Unconditional Prediction}
+\begin{verbatim}
+> data(turnout) 
+> z.out <- zelig(vote ~ race + educate, model = "logit", data = turnout)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Using all observations:
+\begin{verbatim}
+> x.out <- setx(z.out, fn = NULL)
+\end{verbatim}
+Defining a user defined function for {\tt fn}:
+\begin{verbatim}
+> quants <- function(x) {
++  quantile(x, 0.25)
++ }
+> x.out <- setx(z.out, fn = list(numeric = quants))
+\end{verbatim} 
+
+\subsubsection{Example: Conditional Prediction With MatchIt Data}
+Use {\tt demo(match)} to view this example.  
+\begin{verbatim}
+> data(lalonde)
+> match.out <- matchit(treat ~ age + educ + black + hispan + married + 
+                       nodegree + re74 + re75, data = lalonde)
+> z.out <- zelig(re78 ~ pscore, data = match.data(match, "control"), 
+                 model = "ls")
+> x.out <- setx(z.out, data = match.data(match, "treat"), fn = NULL, 
+                cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Example: Conditional Prediction With Multiple Analyses}
+Use {\tt demo(conditional)} to view this example:  
+\begin{verbatim}
+> data(turnout)
+> z.out <- zelig(vote ~ age + educate + income, by = "race",
+                 data = turnout, model = "probit")
+
+# Calculate the ATE (average treatment effect) for race == "white":
+> x.white <- setx(z.out$others, data = turnout[turnout$race == "white",], 
+                  fn = NULL, cond = TRUE)
+> s.others <- sim(z.out$others, x = x.white)
+
+# Calculate the ATE for race == "others":
+> x.others <- setx(z.out$white, data = turnout[turnout$race == "others",], 
+                  fn = NULL, cond = TRUE)
+> s.others <- sim(z.out$white, x = x.others)
+\end{verbatim} %$
+
+\subsubsection{See Also}
+  \begin{itemize}
+  \item \Sref{s:commands} for an overview of the Zelig simulation
+    procedure.
+  \item \Sref{s:models} for an overview of supported models.
+  \item \Sref{s:syntax} for an overview of R syntax.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau ported {\tt setx()} to R, following
+the logic of Clarify (for Stata).  See King, Tomz, and Wittenberg,
+2000.\nocite{KinTomWit00}
+
+Sample data are a selection of $2,000$ observations from
+
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/commands/sim.aux b/inst/doc/commands/sim.aux
new file mode 100644
index 0000000..1b96ffd
--- /dev/null
+++ b/inst/doc/commands/sim.aux
@@ -0,0 +1,41 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {10.3}{\tt  sim}: Simulating Quantities of Interest}{95}{section.10.3}}
+\newlabel{ss:sim}{{10.3}{95}{{\tt sim}: Simulating Quantities of Interest\relax }{section.10.3}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{95}{section*.49}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{95}{section*.50}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{95}{section*.51}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{96}{section*.52}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{97}{section*.53}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{97}{section*.54}}
+\@setckpt{commands/sim}{
+\setcounter{page}{98}
+\setcounter{equation}{0}
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+}
diff --git a/inst/doc/commands/sim.tex b/inst/doc/commands/sim.tex
new file mode 100644
index 0000000..0f46809
--- /dev/null
+++ b/inst/doc/commands/sim.tex
@@ -0,0 +1,152 @@
+\section{{\tt sim}: Simulating Quantities of Interest}\label{ss:sim}
+
+\subsubsection{Description}
+Simulate quantities of interest from the estimated model output from
+\texttt{zelig()} given specified values of explanatory variables
+established in \texttt{setx()}.  For classical \emph{maximum
+  likelihood} models, {\tt sim()} uses asymptotic normal approximation
+to the log-likelihood.  For \emph{Bayesian models}, Zelig simulates
+quantities of interest from the posterior density, whenever possible.
+For \emph{robust Bayesian models}, simulations are drawn from the
+identified class of Bayesian posteriors.  Alternatively, you may
+generate quantities of interest using bootstrapped parameters.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> s.out <- sim(z.out, x, x1 = NULL, num = c(1000, 100), prev = NULL,
+               bootstrap = FALSE,  bootfn = NULL, cond.data = NULL, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+  \item {\tt z.out}: the output object from \texttt{zelig()}.
+  \item {\tt x}: values of explanatory variables used
+    for simulation, generated by {\tt setx()}.  
+  \item {\tt x1}: optional values of explanatory variables (generated
+    by a second call of {\tt setx()}), used to simulate first
+    differences and risk ratios.  (Not available for conditional prediction.)
+  \item {\tt num}: the number of simulations, i.e., posterior draws.
+    If the \texttt{num} argument is omitted, \texttt{sim()} draws 1,000
+    simulations if {\tt bootstrap = FALSE} (the default), or
+    100 simulations if {\tt bootstrap = TRUE}.  You may increase this
+    value to improve accuracy.  (Not available for conditional prediction.)
+  \item {\tt bootstrap:} a logical value indicating if parameters
+    should be generated by re-fitting the model for bootstrapped
+    data, rather than from the likelihood or posterior.  (Not
+    available for conditional prediction.)
+  \item {\tt bootfn:} a function which governs how the data is
+    sampled, re-fits the model, and returns the bootstrapped model
+    parameters.  If {\tt bootstrap = TRUE} and {\tt bootfn = NULL},
+    {\tt sim()} will sample observations from the original data (with
+    replacement) until it creates a sampled dataset with the same
+    number of observations as the original data.  Alternative
+    bootstrap methods (for which users have to specify their own {\tt
+bootfn}) include sampling the residuals rather than the
+    observations, weighted sampling, and parametric bootstrapping.
+    (Not available for conditional prediction.)  
+  \item {\tt \dots}: additional optional arguments passed to
+    \texttt{boot()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+The output stored in {\tt s.out} varies by model.  Use the {\tt
+  names()} command to view the output stored in {\tt s.out}.  Common
+elements include:
+\begin{itemize}
+  \item {\tt x}: the {\tt setx()} values for the explanatory variables,
+    used to calculate the quantities of interest (expected values,
+    predicted values, etc.).    
+  \item {\tt x1}: the optional {\tt setx()} object used to simulate
+    first differences, and other model-specific quantities of
+    interest, such as risk-ratios.  
+  \item {\tt call}: the options selected for {\tt sim()}, used to
+    replicate quantities of interest.  
+  \item {\tt zelig.call}: the original command and options for {\tt
+      zelig()}, used to replicate analyses.
+  \item {\tt num}: the number of simulations requested.  
+  \item {\tt par}: the parameters (coefficients, and additional
+    model-specific parameters).  You may wish to use the same set of
+    simulated parameters to calculate quantities of interest rather
+    than simulating another set.
+  \item {\tt qi\$ev}: simulations of the expected values given the
+    model and {\tt x}.
+  \item {\tt qi\$pr}: simulations of the predicted values given by the
+    fitted values.
+  \item {\tt qi\$fd}: simulations of the first differences (or risk
+    difference for binary models) for the given {\tt x} and {\tt x1}.
+    The difference is calculated by subtracting the expected values
+    given {\tt x} from the expected values given {\tt x1}.  (If do not
+    specify {\tt x1}, you will not get first differences or risk
+    ratios.)
+  \item {\tt qi\$rr}: simulations of the risk ratios for binary and
+    multinomial models.  See specific models for details.    
+  \item {\tt qi\$ate.ev}: simulations of the average expected
+    treatment effect for the treatment group, using conditional
+    prediction. Let $t_i$ be a binary explanatory variable defining
+    the treatment ($t_i=1$) and control ($t_i=0$) groups.  Then the
+    average expected treatment effect for the treatment group is
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{  Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \},
+    \end{equation*} 
+    where $Y_i(t_i=1)$ is the value of the dependent variable for
+    observation $i$ in the treatment group.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+  \item {\tt qi\$ate.pr}: simulations of the average predicted
+    treatment effect for the treatment group, using conditional
+    prediction. Let $t_i$ be a binary explanatory variable defining
+    the treatment ($t_i=1$) and control ($t_i=0$) groups.  Then the
+    average predicted treatment effect for the treatment group is
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \},
+    \end{equation*} 
+    where $Y_i(t_i=1)$ is the value of the dependent variable for
+    observation $i$ in the treatment group.  Variation in the
+    simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+In the case of censored $Y$ in the exponential, Weibull, and lognormal
+models, {\tt sim()} first imputes the uncensored values for $Y$ before
+calculating the ATE.  
+
+You may use the {\tt \$} operator to extract any of the
+above from {\tt s.out}.  For example, {\tt s.out\$qi\$ev} extracts the
+simulated expected values.
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item \Sref{s:commands} for an overview of the Zelig simulation procedure.
+  \item \Sref{s:models} for an overview of supported models.  
+  \item \Sref{s:syntax} for an overview of R syntax.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau generalized {\tt sim()} from a
+similar procedure used in Clarify (for Stata, see King, Tomz, and
+Wittenberg, 2000) and added procedures to bootstrap quantities of
+interest, and perform conditional prediction.  
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/commands/summary.aux b/inst/doc/commands/summary.aux
new file mode 100644
index 0000000..7702323
--- /dev/null
+++ b/inst/doc/commands/summary.aux
@@ -0,0 +1,43 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {10.4}{\tt  summary}: Summarizing Zelig Output}{98}{section.10.4}}
+\newlabel{ss:summary}{{10.4}{98}{{\tt summary}: Summarizing Zelig Output\relax }{section.10.4}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{98}{section*.55}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{98}{section*.56}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments for multiply-imputed {\tt  zelig()} output}{98}{section*.57}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments for subsetted {\tt  zelig()} output}{98}{section*.58}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments for {\tt  sim()} output}{99}{section*.59}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{99}{section*.60}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{99}{section*.61}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{100}{section*.62}}
+\@setckpt{commands/summary}{
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+}
diff --git a/inst/doc/commands/summary.tex b/inst/doc/commands/summary.tex
new file mode 100644
index 0000000..f861dd6
--- /dev/null
+++ b/inst/doc/commands/summary.tex
@@ -0,0 +1,125 @@
+\section{{\tt summary}: Summarizing Zelig Output}
+\label{ss:summary}
+
+\subsubsection{Description}
+
+Summarize R objects using using the generic function {\tt summary()}.
+R automatically recognizes the type of object (lists, data frames,
+variables, etc.) and selects the appropriate {\tt summary()} function.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> summary(z.out, subset = NULL, ...)
+> summary(s.out, subset = NULL, CI = 95, 
+          stats = c("mean", "sd", "min", "max"), ...)
+\end{verbatim}
+To set the desired number of digits in the summary output, use {\tt
+  options(digits = x)}, where {\tt x} is the desired number of digits,
+prior to using the {\tt sumamary()} command.
+
+\subsubsection{Arguments for multiply-imputed {\tt zelig()} output}
+For {\tt zelig()} output created with $m$ multiply-imputed data sets,
+the {\tt z.out} object contains one set of output for each imputed
+data set.  Options for multiply-imputed {\tt zelig()} output include:
+   \begin{itemize}
+   \item {\tt subset}: specifies which of the $m$ sets of output to
+     summarize.  Possible arguments include:
+       \begin{itemize}
+       \item {\tt NULL}: (default) which produces one summary by
+         averaging the coefficients and standard errors from all the
+         imputed data sets using the Rubin rules (\hlink{King,
+           Honaker, Joseph, Scheve
+           (2000)}{http://gking.harvard.edu/files/abs/evil-abs.shtml},
+         p. 53).
+       \item a numeric vector: consisting of any set of numbers in
+         $[1,m]$.  For example, you may use {\tt summary(z.out,
+           subset = 2:4)} to view the output from the second, third,
+         and fourth imputed data sets only.  
+       \end{itemize}
+     \item{\dots}: additional options passed to {\tt print()}.  
+    \end{itemize}
+
+\subsubsection{Arguments for subsetted {\tt zelig()} output}
+If you selected the {\tt by} option, {\tt zelig()} creates an {\tt
+  z.out} object with one set of regression output for each of the
+unique values in the {\tt by} variable.  Options for subsetted
+  analyses are:  
+\begin{itemize}
+\item {\tt subset}: specifies which regression output to summarize.
+  You cannot summarize multiple analyses generated using the {\tt
+    "by"} option in one summary, as each of the levels in the {\tt by}
+  variable may have a different number of associated observations.  (A
+  weighted summary would also be inappropriate, as the weighted
+  coefficients would be identical to the coefficients generated
+  without subsetting.)
+\begin{itemize}
+\item numeric vector: specifying which sets of regression output to
+  summarize.  By default, {\tt summary()} for subsetted output
+  sequentially summarizes the regression output for each unique value
+  in the {\tt by} variable.  You may choose to summarize just the
+  fifteenth unique value by typing:  {\tt summary(z.out, subset =
+    15)}.  
+\end{itemize}
+\item {\tt \dots}: Additional arguments passed to {\tt print()}.
+\end{itemize}
+   
+\subsubsection{Arguments for {\tt sim()} output}
+Summaries of {\tt sim()} output includes these additional options:
+   \begin{itemize}
+     \item {\tt subset}:  Only valid for {\tt sim()} output created
+       using more than one observation in {\tt x}.  You may select to
+       summarize the quantities of interest created by each
+       observation in {\tt x} using one of the following options: 
+       \begin{itemize}
+       \item {\tt NULL}: (default) produces one summary per quantity
+         of interest by combining the simulations from each
+         observation into a single set and then summarizing.
+         \item {\tt all}:  summarizes the quantity of interest
+           produced by each observation separately.  
+         \item a numeric vector: specifying the observations to summarize 
+       \end{itemize}
+     \item {\tt CI}: A value between 0 and 100, for the percentage
+       confidence interval.  The default value is 95, for a 95 percent
+       confidence interval.  
+     \item {\tt stats}: A vector specifying the statistics to
+       calculate for each quantity of interest.  The default values
+       are {\tt c("mean", "sd", "min", "max")}.  
+   \end{itemize}
+Using {\tt summary()} on {\tt sim()} output from multiple analyses
+will automatically weight the quantities of interest according to the
+proportion of observations in each strata.  
+
+\subsubsection{Output Values}
+\begin{itemize}
+\item For {\tt zelig()} output objects, {\tt summary()} returns a
+  formatted summary of the regression output, including the usual
+  table of coefficients, standard errors, and $t$-values.  Additional
+  output depends on the statistical model, the names of which may
+  be viewed using the {\tt names(summary(z.out))} command.
+\item For {\tt sim()} output objects, {\tt summary()} returns summary
+  statistics for each quantity of interest, including a default 95\%
+  confidence interval.  Use {\tt names(summary(s.out))} to see the
+  available output.  Note that {\tt qi.stats} contains a useful matrix
+  of summary statistics for each of the quantities of interest.  For
+  example, {\tt summary(s.out)\$qi.stats\$ev}, extracts the summary
+  matrix of expected values.
+\end{itemize}
+
+\subsubsection{See Also}
+
+Advanced users may wish to refer to {\tt help.zelig("print")},
+as well as {\tt help(summary)}, {\tt help(summary.lm)}, {\tt
+  help(summary.glm)}, and {\tt help(anova)} in the base package.
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau added summary methods for
+certain {\tt zelig()} models, and for {\tt sim()} output.
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
diff --git a/inst/doc/commands/tag.aux b/inst/doc/commands/tag.aux
new file mode 100644
index 0000000..747abd1
--- /dev/null
+++ b/inst/doc/commands/tag.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {13.9}{\tt  tag}: Constrain parameter effects across equations}{315}{section.13.9}}
+\newlabel{tag}{{13.9}{315}{{\tt tag}: Constrain parameter effects across equations\relax }{section.13.9}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{315}{section*.435}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{315}{section*.436}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{315}{section*.437}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{315}{section*.438}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{315}{section*.439}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{315}{section*.440}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{315}{section*.441}}
+\@setckpt{commands/tag}{
+\setcounter{page}{316}
+\setcounter{equation}{0}
+\setcounter{enumi}{2}
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diff --git a/inst/doc/commands/tag.tex b/inst/doc/commands/tag.tex
new file mode 100644
index 0000000..5632b00
--- /dev/null
+++ b/inst/doc/commands/tag.tex
@@ -0,0 +1,54 @@
+\section{{\tt tag}: Constrain parameter effects across equations}
+\label{tag}
+
+\subsubsection{Description}
+Use {\tt tag()} to identify parameters and constrain their effects
+across equations in multiple-equation models.  
+  
+\subsubsection{Syntax}
+\begin{verbatim}
+tag(x, label)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt x}: the variable to be constrained.
+\item {\tt label}: the name that the constrained variable takes.  
+\end{itemize}
+
+\subsubsection{Output Values}
+While there is no specific output from {\tt tag()} itself, {\tt
+parse.formula()} uses {\tt tag()} to identify parameter constraints
+across equations, when a model takes more than one systematic
+component.  
+
+\subsubsection{Examples}
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{ui} for an overview of the multiple-equation user-interface.
+\item \Sref{parse.formula} for more examples of acceptable uses for
+{\tt tag()} in formulas.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, Olivia Lau, and Ferdinand Alimadhi.
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/commands/ternaryplot.aux b/inst/doc/commands/ternaryplot.aux
new file mode 100644
index 0000000..c6756c3
--- /dev/null
+++ b/inst/doc/commands/ternaryplot.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {11.5}{\tt  ternaryplot}: Ternary Diagram for 3D Data}{118}{section.11.5}}
+\newlabel{ternaryplot}{{11.5}{118}{{\tt ternaryplot}: Ternary Diagram for 3D Data\relax }{section.11.5}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{118}{section*.115}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{118}{section*.116}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{118}{section*.117}}
+\citation{Friendly00}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{119}{section*.118}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{119}{section*.119}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{119}{section*.120}}
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+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/commands/ternaryplot.tex b/inst/doc/commands/ternaryplot.tex
new file mode 100644
index 0000000..7be782d
--- /dev/null
+++ b/inst/doc/commands/ternaryplot.tex
@@ -0,0 +1,81 @@
+\section{{\tt ternaryplot}: Ternary Diagram for 3D Data}
+\label{ternaryplot}
+
+\subsubsection{Description} 
+Visualizes compositional, 3-dimensional data in an equilateral
+triangle.   A point's coordinates are found by computing the gravity center
+of mass points using the data entries as weights. Thus, the coordinates
+of a point $P(a,b,c)$ for $a + b + c = 1$ are: $P(b + \frac{c}{2}, \frac{c
+\sqrt{3}}{2})$.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+ternaryplot(x, scale = 1, dimnames = NULL, 
+            dimnames.position = c("corner","edge","none"),
+            dimnames.color = "black", id = NULL, id.color = "black", 
+            coordinates = FALSE, grid = TRUE, grid.color = "gray", 
+            labels = c("inside", "outside", "none"), 
+            labels.color = "darkgray", border = "black", bg = "white",
+            pch = 19, cex = 1, prop.size = FALSE, col = "red", 
+            main = "ternary plot", ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt x}: a matrix with three columns.
+\item {\tt  scale}: row sums scale to be used.
+\item {\tt dimnames}: dimension labels (defaults to the column names
+of {\tt x}).
+\item {\tt dimnames.position, dimnames.color}: position and color of dimension labels.
+\item {\tt id}: optional labels to be plotted below the plot
+symbols. {\tt coordinates} and {\tt id} are mutual exclusive. 
+\item {\tt id.color}:  color of these labels.
+\item {\tt coordinates}: if {\tt TRUE}, the coordinates of the points are
+    plotted below them. {\tt coordinates} and {\tt id} are mutual exclusive.
+\item {\tt grid}: if {\tt TRUE}, a grid is plotted. May optionally
+    be a string indicating the line type (default: {\tt "dotted"}).
+\item {\tt grid.color}: grid color.
+\item {\tt labels, labels.color}: position and color of the grid labels.
+\item {\tt border}: color of the triangle border.
+\item {\tt bg}: triangle background.
+\item {\tt pch}: plotting character. Defaults to filled dots.
+\item {\tt cex}: a numerical value giving the amount by which plotting text
+    and symbols should be scaled relative to the default. Ignored for
+    the symbol size if {\tt prop.size} is not {\tt FALSE}.
+\item {\tt prop.size}: if {\tt TRUE}, the symbol size is plotted
+    proportional to the row sum of the three variables, i.e. represents
+    the weight of the observation.
+\item {\tt col}: plotting color.
+  \item {\tt main}: main title.
+\item {\tt \dots}: additional graphics parameters (see {\tt par}).
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{verbatim}
+data(mexico)
+z.out <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok, 
+                model = "mlogit", data = mexico)
+x.out <- setx(z.out)
+s.out <- sim(z.out, x = x.out)
+
+ternaryplot(s.out$qi$ev, pch = ".", col = "blue",
+            main = "1988 Mexican Presidential Election")
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+\item \Sref{ternarypoints} for a way to add points to an existing
+ternary diagram.  
+\item \Sref{s:plot} for an overview of plotting procedures.  
+\end{itemize} 
+
+\subsubsection{Contributors}
+
+This function was originally written by David Meyer for the vcd
+library, version 0.1, which uses plot graphics, and differs from
+the current implementation in vcd, which uses grid graphics.
+For additional information on vcd, please consult
+\begin{verse}
+\bibentry{Friendly00}.
+\end{verse}
+
diff --git a/inst/doc/commands/ternarypoints.aux b/inst/doc/commands/ternarypoints.aux
new file mode 100644
index 0000000..ad66161
--- /dev/null
+++ b/inst/doc/commands/ternarypoints.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {11.6}{\tt  ternarypoints}: Adding Points to Ternary Diagrams}{120}{section.11.6}}
+\newlabel{ternarypoints}{{11.6}{120}{{\tt ternarypoints}: Adding Points to Ternary Diagrams\relax }{section.11.6}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Description}{120}{section*.121}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{120}{section*.122}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{120}{section*.123}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{120}{section*.124}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{120}{section*.125}}
+\@writefile{toc}{\contentsline {subsubsection}{See Also}{120}{section*.126}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{120}{section*.127}}
+\@setckpt{commands/ternarypoints}{
+\setcounter{page}{121}
+\setcounter{equation}{0}
+\setcounter{enumi}{4}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{2}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{11}
+\setcounter{section}{6}
+\setcounter{subsection}{0}
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+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/commands/ternarypoints.tex b/inst/doc/commands/ternarypoints.tex
new file mode 100644
index 0000000..13e1167
--- /dev/null
+++ b/inst/doc/commands/ternarypoints.tex
@@ -0,0 +1,63 @@
+\section{{\tt ternarypoints}: Adding Points to Ternary Diagrams}
+\label{ternarypoints}
+
+\subsubsection{Description}
+Use {\tt ternarypoints()} to add points to a ternary diagram generated
+using the {\tt ternaryplot()} function in the vcd library.  Use
+ternary diagrams to plot expected values for multinomial choice models
+with three categories in the dependent variable.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> ternarypoints(object, pch = 19, col = "blue", ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item {\tt object}: The input object must be a matrix with three
+  columns.   
+\item {\tt pch}: The selected type of point.  By default, {\tt pch =
+    19}, solid disks.  
+\item {\tt col}: The color of the points.  By default, {\tt col =
+    "blue"}.  
+\item {\tt \dots}: Additional parameters passed to {\tt points()}.  
+\end{itemize}
+
+\subsubsection{Output Values}
+The {\tt ternarypoints()} command adds points to a previously existing
+ternary diagram.  Use {\tt ternaryplot()} to generate the main ternary diagram.  
+
+\subsubsection{Examples}
+\begin{verbatim}
+> ternaryplot(s.out$qi$ev)
+> ternarypoints((s.out$qi$ev + s.out$qi$fd))
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item \Sref{ternaryplot} to create a ternary diagram, to which you
+may add points.  
+  \item \Sref{s:plot} for an overview of plotting procedures
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau created the {\tt
+  ternarypoints()} function to work with {\tt ternaryplot()}.    
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/commands/zelig.tex b/inst/doc/commands/zelig.tex
new file mode 100644
index 0000000..7233f3d
--- /dev/null
+++ b/inst/doc/commands/zelig.tex
@@ -0,0 +1,185 @@
+\section{{\tt zelig}: Estimating a Statistical Model}\label{ss:zelig}
+
+\subsubsection{Description}
+The \texttt{zelig()} command estimates a variety of statistical models.  Use
+{\tt zelig()} output with {\tt setx()} and {\tt sim()} to compute
+quantities of interest, such as predicted probabilities, expected
+values, and first differences, along with the associated measures of
+uncertainty (standard errors and confidence intervals).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(formula, model, data, by = NULL, ...)
+\end{verbatim}
+
+\subsubsection{Arguments}
+\begin{itemize}
+\item{\tt formula}: a symbolic representation of the model to be
+  estimated, in the form {\tt y \~\, x1 + x2}, where {\tt y} is the
+  dependent variable and {\tt x1} and {\tt x2} are the explanatory
+  variables, and {\tt y}, {\tt x1}, and {\tt x2} are contained in the
+  same dataset.  (You may include more than two explanatory variables,
+  of course.)  The {\tt +} symbol means ``inclusion'' not
+  ``addition.''  You may also include interaction terms and main
+  effects in the form {\tt x1*x2} without computing them in prior
+  steps; {\tt I(x1*x2)} to include only the interaction term and
+  exclude the main effects; and quadratic terms in the form {\tt
+    I(x1\^{}2)}.   
+\item{\tt model}: the name of a statistical model, enclosed in {\tt ""}.
+  Currently, the following models are supported:
+  \begin{itemize}
+    \item {\tt "blogit"}: Bivariate logistic regression.  (see
+      \Sref{blogit})
+    \item {\tt "bprobit"}: Bivariate probit regression.  (see
+      \Sref{bprobit})
+    \item {\tt "exp"}: Exponential duration regression.  (see
+      \Sref{exp})
+    \item {\tt "factor.bayes"}: Bayesian factor analysis.  (see
+\Sref{factor.bayes})
+    \item {\tt  "factor.mix"}: Mixed data Bayesian factor analysis.
+(see \Sref{factor.mix})
+    \item {\tt "factor.ord"}: Bayesian factor analysis for ordinal
+variables.  (see \Sref{factor.ord})
+    \item {\tt "ei.dynamic"}: Quinn's dyanamic ecological inference
+model for $2 \times 2$ tables. (see \Sref{ei.dynamic})
+    \item {\tt "ei.hier"}: Bayesian hierarchical ecological inference
+model for a cross-section of $2 \times 2$ tables.  (see
+\Sref{ei.hier})
+%    \item {\tt "ei.RxC"}: Non-linear least squares approximation to
+%the hierarchical Multinomial-Dirichlet ecological inference model in
+%contingency tables with more than 2 rows or columns.  (see \Sref{eiRxC})
+    \item {\tt "gamma"}: Gamma regression.  (see
+      \Sref{gamma})
+    \item {\tt "irt1d"}: Bayesian unidimensional item response theory model.
+(see \Sref{irt1d})
+    \item {\tt "irtkd"}: Bayesian $k$-dimensional item response theory
+model.  (see \Sref{irtkd})
+    \item {\tt "logit"}: Logistic regression.  (see
+      \Sref{logit})
+    \item {\tt "logit.bayes"}: Bayesian logistic regression.  (see
+\Sref{logit.bayes})
+    \item {\tt "lognorm"}: Log-normal duration regression.  (see
+      \Sref{lognorm})
+    \item {\tt "ls"}: Linear least squares regression.  (see
+      \Sref{ls})
+    \item {\tt "mlogit"}: Multinomial logistic regression.  (see
+      \Sref{mlogit})
+    \item {\tt "mlogit.bayes"}: Bayesian multinomial logistic
+regression.  (see \Sref{mlogit.bayes})
+%     \item {\tt "mloglm"}: Multinomial log-linear regression.  (see
+%       \Sref{mloglm})
+%    \item {\tt "mprobit"}: Multinomial probit regression.  (see
+%      \Sref{mprobit})
+    \item {\tt "negbin"}: Negative binomial event count regression.
+      (see \Sref{negbin})
+    \item {\tt "normal"}: Normal linear regression.  (see
+      \Sref{normal})
+    \item {\tt "normal.bayes"}: Bayesian Normal regression (see \Sref{normal.bayes})
+    \item {\tt "ologit"}: Ordinal logistic regression.  (see
+      \Sref{ologit})
+    \item {\tt "oprobit"}: Ordinal probit regression.  (see
+      \Sref{oprobit})
+    \item {\tt "oprobit.bayes"}: Bayesian ordinal probit regression.
+(see \Sref{oprobit.bayes})
+    \item {\tt "poisson"}: Poisson event count regression.  (see
+      \Sref{poisson})
+    \item {\tt "poisson.bayes"}: Bayesian Poisson regression.  (see \Sref{poisson.bayes})
+    \item {\tt "probit"}: Probit regression.  (see
+      \Sref{probit})
+    \item {\tt "probit.bayes"}:  Bayesian Probit regression.  (see \Sref{probit.bayes})
+    \item {\tt "relogit"}: Rare events logistic regression.  (see
+      \Sref{relogit})
+    \item {\tt "tobit"}: Tobit regression.  (see \Sref{tobit})
+    \item {\tt "tobit.bayes"}: Bayesian tobit regression.  (see \Sref{tobit.bayes})
+    \item {\tt "weibull"}: Weibull regression.  (see
+      \Sref{weibull})
+  \end{itemize}
+\item {\tt data}: the name of a data frame containing the variables
+  referenced in the formula, or a list of multiply imputed data frames
+  each having the same variable names and number of rows (created by
+  {\tt mi()}).
+\item {\tt by}: \label{by} A factor variable contained in {\tt
+    data}.  Zelig will subset the data frame based on the levels in
+  the {\tt by} variable, and estimate a model for each subset.  This a
+  particularly powerful option which will allow you to save a
+  considerable amount of effort.  For example, to run the same model
+  on all fifty states, you could type:  
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "state")
+\end{verbatim}
+  You may also use {\tt by} to run models by
+  \hlink{MatchIt}{http://gking.harvard.edu/matchit/} subclass.
+  
+\item{\tt save.data}: the logical variable indicating whether the
+  original data frame should be saved within the output of
+  \texttt{zelig()}. If {\tt FALSE} (default), only the name of the
+  data frame will be stored.
+
+\item{\tt \dots}: additional arguments passed to \texttt{zelig()},
+  depending on the model to be estimated.
+\end{itemize}
+
+\subsubsection{Output Values}
+Depending on the class of model selected, \texttt{zelig()} will return
+an object with elements including \texttt{coefficients}, \texttt{residuals},
+and \texttt{formula} which may be summarized using
+\texttt{summary(z.out)} or individually extracted using, for example,
+\texttt{z.out\$coefficients}.  See the specific models listed above
+for additional output values, or simply type {\tt names(z.out)}.  
+
+\subsubsection{Examples}
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, model = "logit", data = mydata)
+> z.out <- zelig(log(y) ~ x1 + x2, model = "ls", data = mydata)
+> z.out <- zelig(y ~ x1 + I(x2^2), model = "ologit", data = mydata)
+> z.out <- zelig(y ~ x1 * x2, model = "probit", data = mydata)
+> z.out <- zelig(y ~ x1 * x2, model = "probit",  
+                 data = mi(mydata1, mydata2, mydata3, mydata4, mydata5))
+\end{verbatim}
+
+\subsubsection{See Also}
+\begin{itemize}
+  \item \Sref{s:commands} for an overview of the Zelig simulation procedure.
+  \item \Sref{s:models} for an overview of supported models.  
+  \item \Sref{s:syntax} for an overview of R syntax.  
+  \item \Sref{factors} for how to create factor variables.  
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Kosuke Imai, Gary King, and Olivia Lau created the {\tt zelig()}
+framework for handling a selection of the statistical models contained
+in the base, survreg, VGAM, and nnet packages for R.  For additional
+details, please refer to the appropriate model reference page.
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/faq.tex b/inst/doc/faq.tex
new file mode 100644
index 0000000..4b12c6a
--- /dev/null
+++ b/inst/doc/faq.tex
@@ -0,0 +1,293 @@
+\chapter{Frequently Asked Questions}
+
+\section{For All Zelig Users}
+
+\subsection*{How do I cite Zelig?}
+We would appreciate if you would cite Zelig as:  
+\begin{verse}
+  Imai, Kosuke, Gary King and Olivia Lau.  2006.  ``Zelig:
+  Everyone's Statistical Software,''   \url{http://GKing.Harvard.Edu/zelig}.
+\end{verse}
+Please also cite the contributors for the models or methods you are
+using.  These citations can be found in the contributors section of
+each model or command page.
+
+\subsection*{Why can't I install Zelig?}
+
+You must be connected to the internet to install packages from web
+depositories.  In addition, there are a few platform-specific reasons
+why you may have installation problems:
+
+\begin{itemize}
+\item \textbf{On Windows}: If you are using the very latest version of
+  R, you may not be able to install Zelig until we update Zelig to
+  work on the latest release of R. If you wish to install Zelig in the
+  interim, check the Zelig release notes (\Sref{release.notes}) and
+  download the appropriate version of R to work with the last release
+  of Zelig.  You may have to manually download and install Zelig.  
+
+\item \textbf{On Mac or Linux systems}:  If you get the following
+  warning message at the end of your installation:  
+\begin{verbatim}
+Installation of package VGAM had non-zero exit status in ...
+\end{verbatim}
+this means that you were not able to install VGAM properly.  Make sure
+that you have the g77 Fortran compiler.  For PowerPC Macs, download
+g77 from
+\hlink{http://hpc.sourceforge.net}{http://hpc.sourceforge.net}). For
+Intel Macs, download the
+\hlink{xcode}{http://developer.apple.com/tools/xcode/} Apple developer
+tools.  After installation, try to install Zelig again.  
+\end{itemize}
+
+\subsection*{Why can't I install R?}
+
+If you have problems installing R (rather than Zelig), you should
+check the \hlink{R FAQs}{http://cran.r-project.org/faqs.html} for your
+platform.  If you still have problems, you can search the
+\hlink{archives}{https://www.stat.math.ethz.ch/mailman/listinfo/r-help}
+for the R help mailing list, or email the list directly at
+\hlink{r-help at stat.math.ethz.ch}{mailto:r-help at stat.math.ethz.ch}.  
+
+\subsection*{Why can't I load data?}
+
+When you start R, you need to specify your working directory.  In
+linux R, this is done pretty much automatically when you start R,
+whether within ESS or in a terminal window.  In Windows R, you may
+wish to specify a working directory so that you may load data without
+typing in long directory paths to your data files, and it is important
+to remember that \emph{Windows} R uses the \emph{Linux} directory
+delimiter. That is, if you right click and select the ``Properties''
+link on a Windows file, the slashes are backslashes ($\backslash$), but Windows R 
+uses forward slashes ({\tt /}) in directory paths.  Thus, the Windows
+link may be {\tt C:$\backslash$Program Files$\backslash$R$\backslash$\rwvers$\backslash$}, but you would type
+ {\tt C:/Program Files/R/\rwvers/} in Windows R.
+
+When you start R in Windows, the working directory is by default the
+directory in which the R executible is located.
+\begin{verbatim}
+# Print your current working directory.  
+> getwd()                                    
+
+# To read data not located in your working directory. 
+> data <- read.table("C:/Program Files/R/newwork/mydata.tab")
+
+# To change your working directory.  
+> setwd("C:/Program Files/R/newwork")
+
+# Reading data in your working directory.
+> data <- read.data("mydata.tab")
+\end{verbatim}
+Once you have set the working directory, you no longer need to type
+the entire directory path.  
+
+\subsection*{Where can I find old versions of Zelig?} 
+
+For some replications, you may require older versions of Zelig. 
+\begin{itemize}
+\item {\bf Windows} users may find old binaries at
+\url{http://gking.harvard.edu/bin/windows/contrib/}
+and selecting the appropriate version of R.
+
+\item {\bf Linux} and {\bf MacOSX} users may find source files at
+\url{http://gking.harvard.edu/src/contrib/} 
+\end{itemize}  
+If you want an older version of Zelig because you are using an older
+version of R, we strongly suggest that you update R and install the
+latest version of Zelig.  
+
+\subsection*{Some Zelig functions don't work for me!}
+
+If this is a new phenomenon, there may be functions in your
+namespace that are overwriting Zelig functions.  In particular, if you
+have a function called {\tt zelig}, {\tt setx}, or {\tt
+sim} in your workspace, the corresponding functions in Zelig will not
+work.  Rather than deleting things that you need, R will tell you the
+following when you load the Zelig library:
+\begin{verbatim}
+Attaching package: 'Zelig'
+        The following object(s) are masked _by_ .GlobalEnv :
+         sim 
+\end{verbatim}
+In this case, simply rename your {\tt sim} function to something else
+and load Zelig again: 
+\begin{verbatim}
+> mysim <- sim
+> detach(package:Zelig)     
+> library(Zelig)
+\end{verbatim}
+
+\subsection*{Who can I ask for help?  How do I report bugs?}
+
+If you find a bug, or cannot figure something out, please follow these
+steps: (1) Reread the relevant section of \hlink{the
+  documentation}{http://gking.harvard.edu/zelig/docs/}.  (2)
+\hlink{Update
+  Zelig}{http://gking.harvard.edu/zelig/docs/Installation.html} if you
+don't have the current version.  (3) Rerun the same code and see if
+the bug has been fixed.  (4) Check our list
+of \hlink{frequently asked
+  questions}{http://gking.harvard.edu/zelig/docs/Frequently_Asked_Quest.html}.
+(5) \hlink{Search or browse
+  messages}{http://lists.hmdc.harvard.edu/lists/zelig/} to find a
+discussion of your issue on the zelig listserv.
+
+If none of these work, then if you haven't already, please (6)
+\hlink{subscribe to the Zelig
+  listserv}{http://lists.hmdc.harvard.edu/index.cgi?info=zelig} and
+(7) send your question to the listserv at
+\texttt{zelig at latte.harvard.edu}.  Please explain exactly what you did
+and include the full error message, including the {\tt traceback()}.
+You should get an answer from the developers or another user in short
+order.
+
+\subsection*{How do I increase the memory for R?}
+
+Windows users may get the error that R has run out of memory.  
+
+If you have R already installed and subsequently install more RAM, you
+may have to reinstall R in order to take advantage of the additional
+capacity.
+
+You may also set the amount of available memory manually.  Close R,
+then right-click on your R program icon (the icon on your desktop or
+in your programs directory). Select ``Properties'', and then select
+the ``Shortcut'' tab.  Look for the ``Target'' field and after the
+closing quotes around the location of the R executible, add
+\begin{verbatim}
+--max-mem-size=500M
+\end{verbatim}
+as shown in the figure below.  You may increase this value up to 2GB
+or the maximum amount of physical RAM you have installed.
+
+\begin{figure}[h!]
+\begin{center}
+\includegraphics{figs/increase}
+\end{center}
+\end{figure}
+
+If you get the error that R cannot allocate a vector of length x,
+close out of R and add the following line to the ``Target'' field:  
+\begin{verbatim}
+--max-vsize=500M
+\end{verbatim}
+or as appropriate.  
+
+You can always check to see how much memory R has available by typing
+at the R prompt
+\begin{verbatim}
+> round(memory.limit()/2^20, 2)
+\end{verbatim}
+which gives you the amount of available memory in MB.  
+
+%\subsection*{I still run out of memory!  Now what?}
+
+%Most users who run out of memory do so because they choose in-sample
+%prediction ({\tt setx(\dots, fn = NULL, \dots)}).  To get around this, you 
+%can write a loop for {\tt sim()}:  
+
+%\begin{verbatim}
+%z.out <- zelig(...)
+%x.out <- setx(z.out, fn = NULL, ...)
+%s.out <- sim(z.out, x = x.out[1,])
+%for (i in 2:nrow(x.out)) {
+%  tmp <- summary(sim(z.out, x = xout[i,]))
+%  results <- prior.average(results, tmp, i)
+%}
+%results
+%\end{verbatim}
+%where {\tt prior.average()} is a special function we have written to 
+%calculate running summary statistics.  
+
+\subsection*{Why doesn't the pdf print properly?} 
+
+Zelig uses several special \LaTeX\ environments.  If the pdf looks right
+on the screen, there are two possible reasons why it's not printing
+properly:  
+\begin{itemize}
+\item Adobe Acrobat isn't cleaning up the document.  Updating to
+  Acrobat Reader 6.0.1 or higher should solve this problem.  
+\item Your printer doesn't support PostScript Type 3 fonts.  Updating
+  your print driver should take care of this problem.  
+\end{itemize}
+
+\subsection*{R is neat.  How can I find out more?}
+
+R is a collective project with contributors from all over the world.
+Their website
+(\hlink{{\tt http://www.r-project.org}}{http://www.r-project.org}) has more
+information on the R project, R packages, conferences, and other
+learning material.
+
+In addition, there are several canonical references which you may
+wish to peruse:
+\begin{verse}
+Venables, W.N. and B.D. Ripley.  2002. \emph{Modern Applied Statistics with S.} 4th Ed.  Springer-Verlag.  \\
+Venables, W.N. and B.D. Ripley.  2000. \emph{S Programming.} Springer-Verlag.  \\
+\end{verse}
+
+\section{For Zelig Contributors}
+
+\subsection*{Where can I find the source code for Zelig?}
+
+Zelig is distributed under the \hlink{GNU General Public License,
+Version 2}{http://www.gnu.org/licenses/gpl.txt}.  After installation,
+the source code is located in your R library directory.  For Linux
+users who have followed our installation example, this is {\tt
+\~{}/.R/library/Zelig/}.  For Windows users under R \fullrvers, this is by
+default {\tt C:$\backslash$Program Files$\backslash$R$\backslash$\rwvers$\backslash$library$\backslash$Zelig$\backslash$}.
+For Macintosh users, this is {\tt \~{}/Library/R/library/Zelig/}.
+
+In addition, you may download the latest Zelig
+source code as a tarball'ed directory from
+\hlink{{\tt http://gking.harvard.edu/src/contrib/}}{http://gking.harvard.edu/src/contrib/}.  
+(This makes it easier to distinguish functions which are run together during installation.)
+
+\subsection*{How can I make my R programs run faster?}
+
+Unlike most commercial statistics programs which rely on precompiled
+and pre-packaged routines, R allows users to program functions and run
+them in the same environment.  If you notice a perceptible lag when
+running your R code, you may improve the performance of your programs
+by taking the following steps:
+
+\begin{itemize}
+
+\item Reduce the number of loops.  If it is absolutely necessary to run 
+loops in loops, the inside loop should have the most number of cycles
+because it runs faster than the outside loop.  Frequently, you can
+eliminate loops by using vectors rather than scalars. Most R functions
+deal with vectors in an efficient and mathematically intuitive manner.
+
+\item Do away with loops altogether.  You can vectorize functions 
+using the {\tt apply}, {\tt mapply()}, {\tt sapply()}, {\tt lapply()},
+and {\tt replicate()} functions.  If you specify the function passed
+to the above {\tt *apply()} functions properly, the R consensus is that
+they should run significantly faster than loops in general.
+
+\item You can compile your code using C or Fortran.  R is not compiled, 
+but can use bits of precompiled code in C or Fortran, and
+calls that code seamlessly from within R wrapper functions (which pass
+input from the R function to the C code and back to R).  Thus, almost
+every regression package includes C or Fortran algorithms, which are
+locally compiled in the case of Linux systems or precompiled in the
+case of Windows distributions.  The recommended Linux compilers are
+gcc for C and g77 for Fortran, so you should make sure that your code
+is compatible with those standards to achieve the widest possible
+distribution.
+
+\end{itemize}
+
+\subsection*{Which compilers can I use with R and Zelig?}
+
+In general, the C or Fortran algorithms in your package should compile
+for any platform.  While Windows R packages are distributed as
+compiled binaries, Linux R compiles packages locally during
+installation.  Thus, to ensure the widest possible audience for your
+package, you should make sure that your code will compile on gcc (for
+C and C++), or on g77 (for Fortran).  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/figs/increase.eps b/inst/doc/figs/increase.eps
new file mode 100644
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diff --git a/inst/doc/graphs.tex b/inst/doc/graphs.tex
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+\chapter{Graphing Commands}\label{s:plot}
+
+R, and thus Zelig, can produce exceptionally beautiful plots.  Many
+built-in plotting functions exist, including scatter plots, line
+charts, histograms, bar charts, pie charts, ternary diagrams, contour
+plots, and a variety of three-dimensional graphs.  If you desire, you
+can exercise a high degree of control to generate just the right
+graphic.  Zelig includes several default plots for one-observation
+simulations for each model.  To view these plots on-screen, simply
+type {\tt plot(s.out)}, where {\tt s.out} is the output from {\tt
+  sim()}.  Depending on the model chosen, {\tt plot()} will return
+different plots.
+
+If you wish to create your own plots, this section reviews the most
+basic procedures for creating and saving two-dimensional plots.  R
+plots material in two steps:  
+\begin{enumerate}
+\item You must call an output device (discussed in \Sref{ss:output}),
+  select a type of plot, draw a plotting region, draw axes, and plot
+  the given data.  At this stage, you may also define axes labels, the
+  plot title, and colors for the plotted data.  Step one is described
+  in \Sref{ss:draw} below.
+\item Optionally, you may add points, lines, text, or a legend to the
+  existing plot.  These commands are described in \Sref{ss:add}.
+\end{enumerate}
+
+\section{Drawing Plots}\label{ss:draw}
+
+The most generic plotting command is \texttt{plot()}, which
+automatically recognizes the type of R object(s) you are trying to
+plot and selects the best type of plot.  The most common graphs
+returned by \texttt{plot()} are as follows:
+\begin{enumerate}
+\item If \texttt{X} is a variable of length $n$, \texttt{plot(X)}
+  returns a scatter plot of $(x_i, i)$ for $i = 1, \dots n$.  If X is
+  unsorted, this procedure produces a messy graph.  Use
+  \texttt{plot(sort(X))} to arrange the plotted values of $(x_i, i)$
+  from smallest to largest.
+\item With two numeric vectors \texttt{X} and \texttt{Y}, both of
+  length $n$, \texttt{plot(X, Y)} plots a scatter plot of each point
+  $(x_i, y_i)$ for $i = 1, \dots n$.  Alternatively, if Z is an object
+  with two vectors, \texttt{plot(Z)} also creates a scatter plot.
+\end{enumerate}
+
+Optional arguments specific to \texttt{plot} include:
+\begin{itemize}
+\item \texttt{main} creates a title for the graph, and \texttt{xlab}
+  and \texttt{ylab} label the x and y axes, respectively. For
+  example,
+\begin{verbatim}
+plot(x, y, main = "My Lovely Plot", xlab = "Explanatory Variable", 
+     ylab = "Dependent Variable")
+\end{verbatim}
+  
+\item \texttt{type} controls the type of plot you request.  The
+  default is \texttt{plot(x, y, type = "p")}, but you may choose among
+  the following types:
+    \begin{table}[!h]
+    \begin{quote}
+    \begin{tabular}{cl}
+    {\tt "p"} & points\\
+    {\tt "l"} & lines\\
+    {\tt "b"} & both points and lines\\
+    {\tt "c"} & lines drawn up to but not including the points\\
+    {\tt "h"} & histogram\\
+    {\tt "s"} & a step function\\
+    {\tt "n"} & a blank plotting region ( with the axes specified)
+    \end{tabular}
+    \end{quote}
+    \end{table}
+\item If you choose {\tt type = "p"}, R plots open circles by default.
+  You can change the type of point by specifying the {\tt pch}
+  argument.  For example, \texttt{plot(x, y, type = "p", pch = 19)}
+  creates a scatter-plot of filled circles.  Other options for {\tt
+    pch} include:
+    \begin{table}[!h]
+    \begin{quote}
+    \begin{tabular}{cl}
+    19 & solid circle (a disk) \\
+    20 & smaller solid circle \\
+    21 & circle\\
+    22 & square\\
+    23 & diamond \\
+    24 & triangle pointed up\\
+    25 & triangle pointed down\\
+    \end{tabular}
+    \end{quote}
+    \end{table}
+
+In addition, you can specify your own symbols by using, for example,
+{\tt pch = "*"} or {\tt pch = "."}.  
+
+\item If you choose {\tt type = "l"}, R plots solid lines by default.
+  Use the optional {\tt lty} argument to set the line type.  For
+  example, {\tt plot(x, y, type = "l", lty = "dashed")} plots a dashed
+  line.  Other options are dotted, dotdash, longdash, and twodash.
+  
+\item \texttt{col} sets the color of the points, lines, or bars.  For
+  example, \texttt{plot(x, y, type = "b", pch = 20, lty = "dotted",
+    col = "violet")} plots small circles connected by a dotted line,
+  both of which are violet.  (The axes and labels remain black.)  Use
+  \texttt{colors()} to see the full list of available colors.
+  
+\item \texttt{xlim} and {\tt ylim} set the limits to the $x$-axis and
+  $y$-axis.  For example, \texttt{plot(x, y, xlim = c(0, 25), ylim =
+    c(-15, 5))} sets range of the $x$-axis to [0, 25] and the range of
+  the $y$-axis to $[-15, 5]$.
+\end{itemize}
+For additional plotting options, refer to {\tt help(par)}.
+
+\section{Adding Points, Lines, and Legends to Existing Plots}\label{ss:add}
+
+Once you have created a plot, you can \emph{add} points, lines, text,
+or a legend.  To place each of these elements, R uses coordinates
+defined in terms of the x-axes and y-axes of the plot area, not
+coordinates defined in terms of the the plotting window or device.
+For example, if your plot has an x-axis with values between $[0,
+100]$, and a y-axis with values between $[50, 75]$, you may add a
+point at $(55,55)$.  
+
+\begin{itemize}
+\item \textbf{points()} plots one or more sets of points.  Use
+  \texttt{pch} with \texttt{points} to add points to an existing plot.
+  For example, \texttt{points(P, Q, pch = ".", col = "forest green")}
+  plots each $(p_i, q_i)$ as tiny green dots.
+\item \textbf{lines()} joins the specified points with line segments.
+  The arguments \texttt{col} and \texttt{lty} may also be used.  For
+  example, \texttt{lines(X, Y, col = "blue", lty = "dotted")} draws a
+  blue dotted line from each set of points $(x_i, y_i)$ to the next.
+  Alternatively, \texttt{lines} also takes command output which
+  specifies $(x, y)$ coordinates.  For example, \texttt{density(Z)}
+  creates a vector of $x$ and a vector of $y$, and {\tt
+    plot(density(Z))} draws the kernel density function.  
+\item \textbf{text()} adds a character string at the specified set of
+  $(x,y)$ coordinates.  For example, {\tt text(5, 5, labels = "Key
+    Point")} adds the label ``Key Point'' at the plot location
+  $(5,5)$.  You may also choose the font using the {\tt font} option,
+  the size of the font relative to the axis labels using the {\tt cex}
+  option, and choose a color using the {\tt col} option.  The full
+  list of options may be accessed using {\tt help(text)}.   
+\item \textbf{legend()} places a legend at a specified set of $(x,y)$
+  coordinates.  Type {\tt demo(vertci)} to see an example for {\tt
+    legend()}.  
+\end{itemize}
+
+\section{Saving Graphs to Files}\label{ss:output}
+
+By default, R displays graphs in a window on your screen.  To save R
+plots to file (to include them in a paper, for example), preface your
+plotting commands with:
+\begin{verbatim}
+> ps.options(family = c("Times"), pointsize = 12)
+> postscript(file = "mygraph.eps", horizontal = FALSE, paper = "special",
+             width = 6.25, height = 4)
+\end{verbatim}
+where the \texttt{ps.options()} command sets the font type and size in
+the output file, and the \texttt{postscript} command allows you to
+specify the name of the file as well as several additional options.
+Using {\tt paper = special} allows you to specify the width and height
+of the encapsulated postscript region in inches (6.25 inches long and
+4 inches high, in this case), and the statement \texttt{horizontal =
+  FALSE} suppresses R's default landscape orientation.  Alternatively,
+you may use {\tt pdf()} instead of {\tt postscript()}.  If you wish to
+select postscript options for .pdf output, you may do so using options
+in {\tt pdf()}.  For example:
+\begin{verbatim}
+> pdf(file = "mygraph.pdf", width = 6.25, height = 4, family = "Times", 
++     pointsize = 12)
+\end{verbatim}
+
+At the end of every plot, you should close your output device.  The
+command \texttt{dev.off()} stops writing and saves the \texttt{.eps}
+or {\tt .pdf} file to your working directory.  If you forget to close
+the file, you will write all subsequent plots to the same file,
+overwriting previous plots.  You may also use \texttt{dev.off()} to close
+on-screen plot windows.
+
+To write multiple plots to the same file, you can use the following
+options:  
+\begin{itemize}
+\item For plots on separate pages in the same {\tt .pdf} document, use
+\begin{verbatim}
+> pdf(file = "mygraph.pdf", width = 6.25, height = 4, family = "Times", 
++     pointsize = 12, onefile = TRUE)
+\end{verbatim}
+\item For multiple plots on one page, initialize either a {\tt .pdf}
+or {\tt .eps} file, then (before any plotting commands) type:  
+\begin{verbatim}
+par(mfrow = c(2, 4))
+\end{verbatim}
+This creates a grid that has two rows and four columns.  Your plot
+statements will populate the grid going across the first row, then the
+second row, from left to right.  
+\end{itemize}
+
+\newpage
+
+\section{Examples}
+
+\subsection{Descriptive Plots: Box-plots}
+
+\begin{center}
+\includegraphics{figs/sample1}
+\end{center}
+Using the sample \texttt{turnout} data set included with Zelig, the
+following commands will produce the graph above.
+\begin{verbatim}
+> library(Zelig)                             # Loads the Zelig package.
+> data (turnout)                             # Loads the sample data.
+> boxplot(income ~ educate,                  # Creates a boxplot with income
++  data = turnout, col = "grey", pch = ".",  #  as a function of education.  
++  main = "Income as a Function of Years of Education", 
++  xlab = "Education in Years", ylab = "Income in \$10,000s")
+\end{verbatim}
+
+\newpage
+
+\subsection{Density Plots: A Histogram}
+
+Histograms are easy ways to evaluate the density of a quantity of
+interest.  
+
+\begin{center}
+\includegraphics{figs/sample2}
+\end{center}
+
+Here's the code to create this graph:
+\begin{verbatim}
+> library(Zelig)                              # Loads the Zelig package.
+> data(turnout)                               # Loads the sample data set.
+> truehist(turnout$income,  col = "wheat1",   # Calls the main plot, with   
++      xlab = "Annual Income in $10,000s",    #  options.  
++      main = "Histogram of Income") 
+> lines(density(turnout$income))              # Adds the kernel density line.  
+\end{verbatim} %$
+
+\newpage
+
+\subsection{Advanced Examples} 
+
+The examples above are simple examples which only skim the surface of
+R's plotting potential.  We include more advanced, model-specific
+plots in the Zelig demo scripts, and have created functions that
+automate some of these plots, including:  
+
+\begin{enumerate}
+  
+\item {\bf Ternary Diagrams} describe the predicted probability of a
+  categorical dependent variable that has three observed outcomes.
+  You may choose to use this plot with the multinomial logit, the
+  ordinal logit, or the ordinal probit models (Katz and King,
+  1999).\nocite{KatKin99} See \Sref{ternary} for the sample code, type
+  {\tt demo(mlogit)} at the R prompt to run the example, and refer to
+  Section \ref{ternary} to add points to a ternary diagram.
+  
+\begin{center}
+\includegraphics{figs/ternary}
+\end{center}
+
+\newpage
+
+\item {\bf ROC Plots} summarize how well models for binary dependent
+  variables (logit, probit, and relogit) fit the data.  The ROC plot
+  evaluates the fraction of 0's and 1's correctly predicted for every
+  possible threshold value at which the continuous Prob$(Y = 1)$ may
+  be realized as a dichotomous prediction.  The closer the ROC curve
+  is to the upper right corner of the plot, the better the fit of the
+  model specification (King and Zeng, 2002\emph{b})\nocite{KinZen02}.
+  See Section \ref{ROC} for the sample code, and type {\tt demo(roc)} at the
+  R prompt to run the example.
+
+\begin{center}
+\includegraphics{figs/roc}
+\end{center}
+
+\newpage
+
+\item {\bf Vertical Confidence Intervals} may be used for almost any
+  model, and describe simulated confidence intervals for any quantity
+  of interest while allowing one of the explanatory variables to vary
+  over a given range of values (King, Tomz and Wittenberg,
+  2000)\nocite{KinTomWit00}. Type {\tt demo(vertci)} at the R prompt to
+  run the example, and {\tt help.zelig(plot.ci)} for the manual page.
+
+\begin{center}
+\includegraphics{figs/vertci}
+\label{plot.vertci}
+\end{center}
+
+\end{enumerate}
+
+%%% Local Variables: 
+%%% mode: LaTeX
+%%% TeX-master: "zelig"
+%%% End:
+
diff --git a/inst/doc/index.shtml b/inst/doc/index.shtml
new file mode 100644
index 0000000..ba852ce
--- /dev/null
+++ b/inst/doc/index.shtml
@@ -0,0 +1,95 @@
+<html>
+<head>
+<title>Zelig Software Website</title>
+<script language='Javascript' src='/gking.js'></script></HEAD>
+
+<BODY><script language="Javascript">displayHeader()</script>
+
+<h1>Zelig: Everyone's Statistical Software</h1>
+
+<a href="http://www.princeton.edu/~kimai/">Kosuke Imai</a>, 
+<a href="/">Gary King</a>, 
+<a href="http://www.people.fas.harvard.edu/~olau/">Olivia Lau</a>
+<br /><br />
+<!-- rbuild: replace 'Version:' '</b>' version -->
+<b>Version:2.7-1</b>
+
+<table border='0' cellpadding='0' cellspacing='8'><tr><td valign='top' width='200'>
+<p !style="float:left; width: 200; margin: 1.5em;"> <a
+href="/zelig/zelig.jpg"><img src="/zelig/zelig2.jpg" width="200"
+height="285" /></a> <br /><small> Zelig is named after a Woody Allen
+movie about a man who had the strange ability to become the physical
+and psychological reflection of anyone he met and thus to fit
+perfectly in any situation.</small></p>
+
+</td><td valign='top'>
+
+<p>Zelig is a single, easy-to-use program that can estimate, help
+interpret, and present the results of a large range of statistical
+methods. It literally <em>is</em> "everyone's statistical software"
+because Zelig uses (<a href="http://www.r-project.org" target="gkingext">R</a>) code
+from many researchers. We also hope it will <em>become</em>
+"everyone's statistical software" for applications, and we have
+designed it so that anyone can use it or add their methods to it. 
+Zelig comes with detailed, self-contained documentation that minimizes
+startup costs for Zelig and R, automates graphics and summaries for
+all models, and, with only three simple commands required, generally
+makes the power of R accessible for all users. Zelig also works well
+for teaching, and is designed so that scholars can use the same
+program with students that they use for their research.</p>
+
+<p>Zelig adds considerable infrastructure to improve the use of
+existing methods. It generalizes the program <a
+href="/stats.shtml#clarify">Clarify</a> (for Stata), which translates
+hard-to-interpret coefficients into quantities of interest; combines
+multiply imputed data sets (such as output from <a
+href="/stats.shtml#amelia">Amelia</a>) to deal with missing data;
+automates bootstrapping for all models; uses sophisticated
+nonparametric matching commands which improve parametric procedures
+(via <a href="/matchit/">MatchIt</a>); allows one-line commands to run
+analyses in all designated strata; automates the creation of
+replication data files so that you (or, if you wish, anyone else) can
+replicate the results of your analyses (hence satisfying the <a
+href="/projects/repl.shtml">replication standard</a>); makes it easy
+to evaluate counterfactuals (via <a href="/whatif">WhatIf</a>); and
+allows conditional population and superpopulation inferences. Zelig
+includes many specific methods, based on likelihood, frequentist,
+Bayesian, robust Bayesian, and nonparametric theories of
+inference.</p>
+
+<ul>
+<li>Documentation:  <a href="/zelig/docs/">Read on-line</a> or <a
+href="/zelig/docs/zelig.pdf">Download PDF</a></li>
+
+<li><a href="/zelig/docs/Installation.html">Installation</a>, <a
+href="/zelig/docs/Frequently_Asked_Quest.html">Frequently Asked
+Questions</a></li>
+
+<li>All questions, bugs and requests: Zelig Mailing List, <a
+href="http://lists.hmdc.harvard.edu/index.cgi?info=zelig"
+target="gkingext">[Un]Subscribe</a>, or <a
+href="http://lists.hmdc.harvard.edu/lists/zelig/"
+target="gkingext">Browse/Search Archives</a></li>
+
+<li>A paper that describes the advances underlying Zelig software:
+Kosuke Imai, Gary King, and Olivia Lau. <span class="bg">"Toward A
+Common Framework for Statistical Analysis and Development"</span>
+(<span class="bg">Abstract:</span> <a
+href="/files/abs/z-abs.shtml">HTML</a> | <span class="bg">Paper:</span> <a
+href="/files/z.pdf">PDF</a>)
+    
+<li><a href="/files/zelig_shortcourse.pdf">Slides used to introduce
+Zelig</a></li>
+
+<li><a class="monitor" href="/zelig/" target="ChangeDetectionWiz"
+    onClick="doChangeDetectionPopup()"> Track Zelig changes</a></li>
+</ul>
+
+</td>
+</tr>
+</table>    
+    
+   
+
+<script language="Javascript">displayFooter()</script></BODY>
+</html>
diff --git a/inst/doc/inference/gee.tex b/inst/doc/inference/gee.tex
new file mode 100644
index 0000000..49cf1b9
--- /dev/null
+++ b/inst/doc/inference/gee.tex
@@ -0,0 +1,76 @@
+\subsection{General Estimating Equation}
+
+Consider a logitudinal data set where $Y_{it}$ denotes the scalar
+outcome variable and $X_{it}$ represents an $(k \times 1)$ vector of
+explanatory variables for each individual $i$ ($i=1,\dots,N$) at each
+time period $t$ ($t=1,\dots,T$).  The stochastic component is
+given by
+\begin{eqnarray*}
+  Y_{it} & \sim & f(y_{it}\mid \mu_{it}, \sigma^2_{it})
+\end{eqnarray*}
+where $y_{it}$ is a realization of $Y_{it}$, $\mu_{it}$ is the mean,
+and $\sigma^2_{it}$ is the variance of $Y_{it}$.  Then the two
+systematic components model the mean and variance as follows
+\begin{eqnarray*}
+\mu_{it} & \equiv & E(Y_{it}\mid X_i) \; = \; g^{-1}(X_i^\top \beta), \\
+\sigma^2_{it} & \equiv & {\rm Var}(Y_{it}\mid \mu_{it}) \; = \; \phi\, v(\mu_{it}).
+\end{eqnarray*}
+where $\beta$ is the vector of coefficients, and $\phi$ is a
+dispersion parameter.  The fucntions $g(\cdot)$ and $v(\cdot)$ are
+known, and are called the ``link function'' and ``variance function,''
+respectively.  GEE models may permit a third systematic component that
+allows the covariance to vary over individuals as
+\begin{eqnarray*}
+  {\rm Cov}(Y_i) = A_i^{\frac{1}{2}} R(\alpha) A_i^{\frac{1}{2}},
+\end{eqnarray*}
+where $R(\alpha)$ is the $(T \times T)$ correlation matrix (all
+diagonal elements equal to 1) and is a function of a parameter
+$\alpha$, and $A_i$ is a diagonal matrix of variances
+$\sigma^2_{it}=\phi v(\mu_{it})$. There are various ways to specify
+the correlation structure:
+\begin{itemize}
+\item Independence (\texttt{constr = "independence"}): ${\rm
+    Cor}(Y_{it}, Y_{it'})=0$ for all $t$ and $t'$ with $t\ne t'$.
+\item Exchangeable (\texttt{constr = "exchangeable"}): ${\rm
+    Cor}(Y_{it}, Y_{it'})=\alpha$ for all $t$ and $t'$ with $t\ne t'$.
+\item $m$th order autoregressive (\texttt{constr = "AR-M"} and
+  \texttt{Mv} must be set to $m$): For example, the first order
+  autoregressive model implies ${\rm Cor}(Y_{it},
+  Y_{it'})=\alpha^{|t-t'|}$ for all $t$ and $t'$ with $t\ne t'$.
+\item Stationary $m$ dependent (\texttt{constr = "stat\_M\_dep"} and
+  \texttt{Mv} must be set to $m$}):
+  $${\rm Cor}(Y_{it}, Y_{it'})=\left\{\begin{array}{ccc}
+      \alpha_{|t_j-t_j'|} & {\rm if} & |t_j-t_j'|\le m \\ 0 & {\rm if}
+      & |t_j-t_j'| > m
+    \end{array}\right.$$
+\item Non-stationary $m$ dependent (\texttt{constr =
+    "non\_stat\_M\_dep"} and \texttt{Mv} must be set to $m$): {\bf I'm
+    not sure what this is!}
+\item Unstructured (\texttt{constr = "unstructured"}): ${\rm
+    Cor}(Y_{it}, Y_{it'})=\alpha_{tt'}$ for all $t$ and $t'$ with
+  $t\ne t'$.
+\end{itemize}
+
+Then, the GEE estimator is a solution to the following estimating
+equation,
+\begin{eqnarray*}
+\sum_{i=1}^N D_i^\top V_i (Y_i - \mu_i) & = & 0
+\end{eqnarray*}
+where $Y_i=(Y_{i1}, Y_{i2}, \dots, Y_{iN})$ and $\mu_i=(\mu_{i1},
+\mu_{i2}, \dots,\mu_{iN})$ are vectors of length $T$, and $D_i = d
+\mu_i/ d\beta$.  The consistency of the GEE estimator for $\beta$ does
+not depend on the specification of the correlation matrix. That is,
+even with a misspecified correlation matrix, the estimate of $\beta$
+is consistent. However, the correct correlation matrix specification
+will make estimation efficient, and the closer the specified
+correlation structure is to the true structure, the more efficient is
+the estimator.  GEE also utilizes the sandwich estimator to produce
+robust standard errors, which produces consistent standard errors even
+if the correlation and variance specifications are incorrect.
+
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/research/zelig/docs/zelig"
+%%% End: 
diff --git a/inst/doc/install.R b/inst/doc/install.R
new file mode 100644
index 0000000..b845ab9
--- /dev/null
+++ b/inst/doc/install.R
@@ -0,0 +1,12 @@
+if (! "MASS" %in% installed.packages()[,"Package"])
+  install.packages("MASS", CRAN = "http://cran.cnr.berkeley.edu/")
+
+install.packages("Zelig", repos = "http://cran.us.r-project.org/")
+##install.packages("Zelig", repos = "http://gking.harvard.edu")
+install.packages("zoo", repos = "http://cran.cnr.berkeley.edu/")
+install.packages("sandwich", repos = "http://cran.cnr.berkeley.edu/")
+install.packages("MCMCpack", repos = "http://cran.cnr.berkeley.edu/")
+install.packages("coda", repos = "http://cran.cnr.berkeley.edu/")
+install.packages("lattice", repos = "http://cran.cnr.berkeley.edu/")
+install.packages("mvtnorm", repos = "http://cran.cnr.berkeley.edu/")
+install.packages("VGAM", repos = "http://cran.cnr.berkeley.edu/")
diff --git a/inst/doc/install.tex b/inst/doc/install.tex
new file mode 100644
index 0000000..9599182
--- /dev/null
+++ b/inst/doc/install.tex
@@ -0,0 +1,426 @@
+\chapter{Installation}
+
+To use Zelig, you must install the statistical program R (if it is not
+already installed), the Zelig package, and some R libraries (coda,
+MCMCpack, sandwich, VGAM, and zoo).  
+
+Note: In this document, {\tt >} denotes the R prompt.  
+
+\subsubsection{If You Know R} 
+
+We recommend that you launch R and type 
+\begin{verbatim}
+> source("http://gking.harvard.edu/zelig/install.R")
+> library(Zelig)
+\end{verbatim}
+then proceed to \Sref{overview}.  For Windows R, you may edit the 
+{\tt Rprofile} file to load Zelig automatically at launch (after which you
+will no longer need to type {\tt library(Zelig)} at startup).  Simply
+add the line:
+\begin{verbatim}
+options(defaultPackages = c(getOption("defaultPackages"), "Zelig"))
+\end{verbatim}
+
+\subsubsection{If You Are New to R}
+
+If you are new to R, we recommend that you read the following section
+on installation procedures as well as the overview of R syntax and
+usage in \Sref{a:R}.
+
+This distribution works on a variety of platforms, including Windows
+(see \Sref{ss:win}), MacOSX (see \Sref{ss:osx}), and Linux (see
+\Sref{ss:unix}).  Alternatively, you may access R from your PC using a
+terminal window or an X-windows tunnel to a Linux or Unix server (see
+\Sref{ss:unix}).  Most servers have R installed; if not, contact your
+network administrator.
+
+There are advantages and disadvantages to each type of installation.
+On a personal computer, R is easier to install and launch.  Using R
+remotely on a server requires a bit more set-up, but does not tie up
+your local CPU, and allows you to take advantage of the server's speed.
+
+\section{Windows}\label{ss:win}
+
+\subsubsection{Installing R}
+
+Go to the Comprehensive R Archive Network website
+\url{(http://www.r-project.org)} and download the latest
+\hlink{installer for
+  Windows}{http://cran.us.r-project.org/bin/windows/base/} at
+\hlink{\url{http://cran.us.r-project.org/bin/windows/base/}}{http://cran.us.r-project.org/bin/windows/base/}
+Double-click the {\tt .exe} file to launch the R installer.  We recommend
+that you accept the default installation options if this your first
+installation.
+
+\subsubsection{Installing Zelig}
+
+Once R is installed, you must install the Zelig and VGAM packages.  There
+are three ways to do this.  
+\begin{enumerate}
+\item We recommend that you start R and then type:
+\begin{verbatim}
+> source("http://gking.harvard.edu/zelig/install.R")
+> library(Zelig)
+\end{verbatim}
+\item Alternatively, you may install each component package individually 
+in R:
+\begin{verbatim}
+> install.packages("Zelig")
+> install.packages("MCMCpack")
+> install.packages("coda")
+> install.packages("VGAM")
+> install.packages("zoo")
+> install.packages("sandwich")
+> library(Zelig)
+\end{verbatim}
+Zelig will load the optional libraries whenever their functions are
+needed; it is not necessary to load any package other than Zelig at startup.  
+
+\item \label{manual.windows}Alternatively, you may use the drop down
+  menus to install Zelig.  This requires four steps.
+\begin{enumerate}
+\item \label{win.zelig} Go to \hlink{the Zelig
+    website}{http://gking.harvard.edu/bin/windows/contrib/\rvers/} and
+  and download the latest release of Zelig.  The VGAM, MCMCpack,
+coda, zoo, and sandwich packages are available from
+\hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}.  Store these
+  \texttt{.zip} files in your R program directory.  For example, the
+  default R program directory is {\tt C:$\backslash$Program
+  Files$\backslash$R$\backslash$\rwvers$\backslash$}.\footnote{Note
+  that when updating R to the latest release, the installer does not
+  delete previous versions from your {\tt C:$\backslash$Program
+  Files$\backslash$R$\backslash$} directory.  In this example, the
+  subdirectory {\tt $\backslash$\rwvers$\backslash$} stores R version
+  \fullrvers.  Thus, if you have a different version of R installed,
+  you should change the last part of the R program directory file path
+  accordingly.}
+\item Start R.  From the drop-down menus, select the ``Packages'' menu
+  and then the ``Install Files from Local Zip Files'' option.
+\item A window will pop up, allowing you to select one of the
+  downloaded files for installation.  There is no need to unzip the
+  files prior to installation.  Repeat and select the other downloaded
+  file for installation.
+\item At the R prompt, type \texttt{library(Zelig)} to load the
+  functionality described in this manual.  Note that Zelig will
+  automatically load the other libraries as necessary.
+\end{enumerate}
+
+\item An additional \emph{recommended but optional step} is to set up
+  R to load Zelig automatically at launch.  (If you skip this step,
+  you must type {\tt library(Zelig)} at the beginning of every R
+  session.)  To automate this process, edit the {\tt Rprofile} file
+  located in the R program subdirectory ({\tt C:$\backslash$Program
+    Files$\backslash$R$\backslash$\rwvers$\backslash$etc$\backslash$}
+  in our example).  Using a text editor such as Windows notepad, add
+  the following line to the {\tt Rprofile} file:
+\begin{verbatim}
+options(defaultPackages = c(getOption("defaultPackages"), "Zelig"))
+\end{verbatim}
+\end{enumerate}
+
+Zelig is distributed under the \hlink{GNU General Public License,
+  Version 2}{http://www.gnu.org/licenses/gpl.txt}.  After
+installation, the source code is located in your R library directory,
+which is by default {\tt C:$\backslash$Program
+  Files$\backslash$R$\backslash$\rwvers$\backslash$library$\backslash$Zelig$\backslash$}.
+
+\subsubsection{Updating Zelig}
+
+There are two ways to update Zelig.
+\begin{enumerate}
+\item We recommend that you periodically update Zelig at the R prompt
+  by typing:
+\begin{verbatim}
+> update.packages()
+> library(Zelig)
+\end{verbatim}
+\item Alternatively, you may use the procedure outlined in
+  \Sref{win.zelig} to periodically update Zelig.  Simply download the
+  latest {\tt .zip} file and follow the four steps.
+\end{enumerate}
+
+\section{MacOS X}\label{ss:osx}
+
+\subsubsection{Installing R}  
+
+If you are using MacOS X, you may install the latest version of R
+(\fullrvers\ at this time) from the CRAN website \hlink{
+  http://cran.us.r-project.org/bin/macosx/}{http://cran.us.r-project.org/bin/macosx/}.
+At this time, Zelig is not supported for R on MacOS 8.6 through 9.x.
+
+\subsubsection{Installing Zelig}\label{osx.manual}
+
+Once R is installed, you must install the Zelig and VGAM packages.
+There are several ways to do this.
+\begin{enumerate}
+\item {\bf For RAqua}:
+  \begin{enumerate}
+    
+  \item We recommend that you start R, and then type:
+\begin{verbatim}
+> source("http://gking.harvard.edu/zelig/install.R")
+> library(Zelig)
+\end{verbatim}
+(You may ignore the warning messages, unless they say ``Non-zero exit 
+status''.)
+\item Alternatively, to avoid the warning messages, you need to install 
+each package individually and specify the specific installation path:
+\begin{verbatim}
+> install.packages("Zelig", lib = "~/Library/R/library")
+> install.packages("MCMCpack", lib = "~/Library/R/library")
+> install.packages("coda", lib = "~/Library/R/library")
+> install.packages("VGAM", lib = "~/Library/R/library")
+> install.packages("zoo", lib = "~/Library/R/library")
+> install.packages("sandwich", lib = "~/Library/R/library")
+> library(Zelig)
+\end{verbatim}
+    where \texttt{\~{}/Library/R/library} is the default local library
+    directory. Zelig will load the other libraries whenever their functions
+    are needed; it is not necessary to load these packages at startup.
+    
+  \item Alternatively, you may use the drop down menus to install
+    Zelig.  This requires three steps.  
+  \begin{enumerate} 
+  \item Go to \hlink{the Zelig
+      website}{http://gking.harvard.edu/src/contrib/} and 
+    download the latest release of Zelig.  The VGAM, MCMCpack,
+coda, zoo, and sandwich packages are available from
+\hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}.
+Save these \texttt{.tar.gz} files in a convenient place.
+  \item Start R.  From the drop-down menus, select the ``Packages''
+    menu and then the ``Install Files from Local Files'' option.
+  \item A window will pop up, allowing you to select the one of the
+    downloaded files for installation.  There is no need to unzip
+    the files prior to installation.  Repeat and select the other
+    downloaded file for installation.
+  \end{enumerate} 
+  
+\end{enumerate}
+
+\item {\bf For command line R:} 
+  \begin{enumerate}
+  \item Before installing command line R, you need to create a local R
+    library directory.  If you have done so already, you may skip to
+    the next step.  Otherwise, at the terminal prompt in your home
+    directory, type:
+\begin{verbatim}
+% mkdir ~/Library/R ~/Library/R/library
+\end{verbatim}
+  \item Modify your configuration file to identify {\tt
+      \~{}/Library/R/library} as your R library directory.  There are
+    two ways of doing this:
+    \begin{enumerate}
+    \item Open the {\tt .Renviron} file (or create one, if you don't
+      have one) and add the following line:
+\begin{verbatim} 
+R_LIBS = "~/Library/R/library"
+\end{verbatim}
+    \item {\it Alternatively}, you may modify your shell configuration
+      file.  For a Bash shell, open your {\tt .bashrc} file and add
+      the following line:
+\begin{verbatim}
+export R_LIBS="$HOME/Library/R/library"
+\end{verbatim} %$
+      \end{enumerate}
+    \item Start R and at the prompt, type:
+\begin{verbatim}
+> source("http://gking.harvard.edu/zelig/install.R")
+> library(Zelig)
+\end{verbatim}
+(You may ignore the warning messages, unless they say ``Non-zero exit 
+status''.)
+\item Alternatively, to avoid the warning messages, you need to install 
+each component package separately and specify the installation path:
+\begin{verbatim}
+> install.packages("Zelig", lib = "~/Library/R/library")
+> install.packages("MCMCpack", lib = "~/Library/R/library")
+> install.packages("coda", lib = "~/Library/R/library")
+> install.packages("VGAM", lib = "~/Library/R/library")
+> install.packages("zoo", lib = "~/Library/R/library")
+> install.packages("sandwich", lib = "~/Library/R/library")
+> library(Zelig)
+\end{verbatim}
+      Although the {\tt lib} argument is optional, we recommend that
+      you set it to the default RAqua directory
+      (\verb|"~/Library/R/library"|), in case you later decide to
+      install the RAqua GUI (which has a different default directory).
+      \end{enumerate}
+\end{enumerate}
+  
+At the R prompt, type \texttt{library(Zelig)} to load the
+functionality described in this manual.  Note that Zelig will
+automatically load the other packages as necessary.
+  
+Zelig is distributed under the \hlink{GNU General Public License,
+  Version 2}{http://www.gnu.org/licenses/gpl.txt}.  After
+installation, the source code is located in your R library directory,
+{\tt \~{}/Library/R/library/Zelig/}.
+
+\subsubsection{Updating Zelig}
+  
+There are two ways to update Zelig.
+\begin{enumerate}
+
+\item We recommend that you start R and, at the R prompt, type:
+\begin{verbatim}
+> update.packages()
+\end{verbatim}
+  
+\item Alternatively, you may remove an old version by command by
+  typing {\tt R CMD REMOVE Zelig} at the terminal prompt.  Then
+  download and reinstall the package using the installation
+  procedures~\Sref{osx.manual} outlined above.
+\end{enumerate}
+
+\section{UNIX and Linux}\label{ss:unix}
+
+\subsubsection{Installing R}
+Type {\tt R} at the terminal prompt (which we denote as {\tt \%} in
+this section) to see if R is available. (Typing \texttt{q()} will
+enable you to quit.)  If it is installed, proceed to the next section.
+If it is not installed and you are not the administrator, contact that
+individual, kindly request that they install R on the server, and
+continue to the next section. If you have administrator privileges,
+you may download the latest release at the
+\hlink{CRAN}{http://cran.r-project.org} website.  Although
+installation varies according to your Linux distribution, we provide
+an example for Red Hat Linux 9.0 as a guide:
+
+\begin{enumerate}
+
+\item Log in as root.
+
+\item Download the appropriate binary file for Red Hat 9 from CRAN.
+  For example, for Red Hat 9 running on the Intel 386 platform, go to
+  \url{http://cran.r-project.org/bin/linux/}. 
+
+\item Type the following command at the terminal prompt:\newline {\tt
+    \% rpm -ivh R-\fullrvers-1.i386.rpm}
+
+\end{enumerate}
+
+\subsubsection{Installing Zelig}\label{sss:unix.library}
+
+Before installing Zelig, you need to create a local R library
+directory. If you have done so already, you can skip to
+\Sref{unix.zelig}. If not, you must do so before proceeding because
+most users do not have authorization to install programs globally.
+Suppose we want the directory to be {\tt \~{}/.R/library}.  At the
+terminal prompt in your home directory, type:
+\begin{verbatim}
+% mkdir ~/.R ~/.R/library
+\end{verbatim}
+Now you are ready to install Zelig.\label{unix.zelig} There are two
+ways to proceed.
+
+\begin{enumerate}
+\item Recommended procedure: 
+  \begin{enumerate}
+  \item Open the {\tt \~\,/.Renviron} file (or create it if it does
+    not exist) and add the following line:
+\begin{verbatim}
+R_LIBS = "~/.R/library"
+\end{verbatim}
+    You only need to perform this step once.
+  \item Start R.  At the R prompt, type:
+\begin{verbatim}
+> source("http://gking.harvard.edu/zelig/install.R")
+> library(Zelig)
+\end{verbatim}
+(You may ignore the warning messages, unless they say ``Non-zero exit 
+status''.)
+\item Alternatively, you can avoid the warning messages by installing each 
+component package separately and specifying the installation path:  
+\begin{verbatim}
+> install.packages("Zelig", lib = "~/.R/library")
+> install.packages("MCMCpack", lib = "~/.R/library")
+> install.packages("coda", lib = "~/.R/library")
+> install.packages("VGAM", lib = "~/.R/library")
+> install.packages("zoo", lib = "~/.R/library")
+> install.packages("sandwich", lib = "~/.R/library")
+> library(Zelig)
+\end{verbatim}
+\item Finally, create a {\tt .Rprofile} file in your home directory, containing the line:
+\begin{verbatim}
+library(Zelig)
+\end{verbatim}
+This will load Zelig every time you start R.  
+  \end{enumerate}  
+
+\item \label{unix.manual} Alternatively: 
+  \begin{enumerate}
+  \item Add the local R library directory that you created above ({\tt
+      \~\,/.R/library} in the example) to the environmental variable
+    {\tt R\_LIBS}.
+%  If you do not know how to
+%    define an environmental variable, download the script {\tt
+%      Rloclib} from the \hlink{Zelig download
+%      directory}{http://gking.harvard.edu/R/CRAN/src/contrib/}, and
+%    run it by typing at the terminal prompt:
+%\begin{verbatim}
+% bash Rloclib [path]
+%\end{verbatim}
+%    where {\rm [path]} is an optional input for the local library
+%    directory path. If you don't specify a path, the default value is
+%    {\tt \~\,/.R/library}.
+  \item Download the latest bundles for Unix from the \hlink{Zelig
+      website}{http://gking.harvard.edu/R/CRAN/src/contrib/}, and (for
+    the VGAM, MCMCpack, coda, sandwich, and zoo packages) from the
+    \hlink{CRAN}{http://cran.r-project.org/src/contrib/PACKAGES.html}
+    website.
+  \item If {\tt XX} is the current version number, at the terminal
+    prompt, type:
+\begin{verbatim}
+% R CMD INSTALL Zelig_XX.tar.gz
+% R CMD INSTALL MCMCpack_XX.tar.gz
+% R CMD INSTALL coda_XX.tar.gz
+% R CMD INSTALL VGAM_XX.tar.gz
+% R CMD INSTALL zoo_XX.tar.gz
+% R CMD INSTALL sandwich_XX.tar.gz
+% rm Zelig_XX.tar.gz VGAM_XX.tar.gz zoo_XX.tar.gz sandwich_XX.tar.gz
+\end{verbatim}
+\item Create a {\tt .Rprofile} file in your home directory, containing the line:  
+\begin{verbatim}
+library(Zelig)
+\end{verbatim}
+This will load Zelig every time you start R.  
+  \end{enumerate}
+\end{enumerate}
+
+Zelig is distributed under the \hlink{GNU General Public License,
+  Version 2}{http://www.gnu.org/licenses/gpl.txt}.  After
+installation, the source code is located in your R library directory.
+If you followed the example above, this is {\tt ~/.R/library/Zelig/}.
+
+\subsubsection{Updating Zelig} \label{sss:unix.updating}
+
+There are two ways to update Zelig.  
+
+\begin{enumerate}
+
+\item We recommend that you start R and, at the R prompt, type:
+\begin{verbatim}
+> update.packages()
+\end{verbatim}
+  
+\item Alternatively, you may remove an old version by command by
+  typing {\tt R CMD REMOVE Zelig} at the terminal prompt.  Then
+  download and reinstall the package using the installation
+  procedure Section \ref{sss:unix.library} outlined above.
+
+\end{enumerate}
+
+\section{Version Compatability}
+
+In addition to R itself, Zelig also depends on several R packages
+maintained by other development teams.  Although we make every effort
+to keep the latest version of Zelig up-to-date with the latest version
+of those packages, there may occasionally be incompatabilities.  See
+\ref{table.compat} in the Appendix for a list of packages tested to be
+compatabile with a given Zelig release.  You may obtain older versions
+of most packages at \url{http://www.r-project.org}.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/intro.tex b/inst/doc/intro.tex
new file mode 100644
index 0000000..5dd0843
--- /dev/null
+++ b/inst/doc/intro.tex
@@ -0,0 +1,137 @@
+\chapter{Introduction}
+
+\section{What Zelig and R Do}
+
+Zelig\footnote{Zelig is named after a Woody Allen movie about a man
+  who had the strange ability to become the physical reflection of
+  anyone he met --- Scottish, African-American, Indian, Chinese, thin,
+  obese, medical doctor, Hassidic rabbi, anything --- and thus to fit
+  well in any situation.} is an easy-to-use program that can estimate
+and help interpret the results of an enormous and growing range of
+statistical models.  It literally \emph{is} ``everyone's statistical
+software'' because Zelig's unified framework incorporates everyone
+else's (R) code.  We also hope it will \emph{become} ``everyone's
+statistical software'' for applications, and we have designed Zelig so
+that anyone can use it or add their models to it.
+
+When you are using Zelig, you are also using
+\hlink{R}{http://www.r-project.com}, a powerful statistical software
+language.  You do not need to learn R separately, however, since this
+manual introduces you to R through Zelig, which simplifies
+R and reduces the amount of programming knowledge you need to
+get started.  Because so many individuals contribute different
+packages to R (each with their own syntax and documentation),
+estimating a statistical model can be a frustrating experience.  Users
+need to know which package contains the model, find the modeling
+command within the package, and refer to the manual page for the
+model-specific arguments.  In contrast, Zelig users can skip these
+start-up costs and move directly to data analyses.  Using Zelig's
+unified command syntax, you gain the convenience of a packaged
+program, without losing any of the power of R's underlying statistical
+procedures.
+
+In addition to generalizing R packages and making existing methods
+easier to use, Zelig includes infrastructure that can improve all
+existing methods and R programs.  Even if you know R, using Zelig
+greatly simplifies your work.  It mimics the popular
+\hlink{Clarify}{http://gking.harvard.edu/stats.shtml\#clarify} program
+for Stata (and thus the suggestions of \hlink{King, Tomz, and
+  Wittenberg,
+  2000}{http://gking.harvard.edu/files/abs/making-abs.shtml})
+\nocite{KinTomWit00} by translating the raw output of existing statistical
+procedures into quantities that are of direct
+interest to researchers.  Instead of trying to interpret coefficients
+parameterized for modeling convenience, Zelig makes it easy to compute
+quantities of real interest: probabilities, predicted values, expected
+values, first differences, and risk ratios, along with confidence
+intervals, standard errors, or full posterior (or sampling) densities
+for all quantities.  Zelig extends Clarify by seamlessly integrating
+an option for bootstrapping into the simulation of quantities of
+interest.  It also integrates a full suite of nonparametric matching
+methods as a preprocessing step to improve the performance of any
+parametric model for causal inference (see
+\hlink{MatchIt}{http://gking.harvard.edu/matchit}).  For missing data,
+Zelig accepts multiply imputed datasets created by
+\hlink{Amelia}{http://gking.harvard.edu/stats.shtml\#amelia} (see
+\hlink{King, Honaker, Joseph, and Scheve,
+  2001}{http://gking.harvard.edu/files/abs/evil-abs.shtml})\nocite{KinHonJos01}
+and other programs, allowing users to analyze them as if they were a
+single, fully observed dataset.  Zelig outputs replication data sets
+so that you (and if you wish, anyone else) will always be able to
+replicate the results of your analyses (see \hlink{King,
+  1995}{http://gking.harvard.edu/files/abs/replication-abs.shtml}).\nocite{King95}
+Several powerful Zelig commands also make running multiple analyses
+and recoding variables simple.
+
+Using R in combination with Zelig has several advantages over
+commercial statistical software.  R and Zelig are part of the open
+source movement, which is roughly based on the principles of science.
+That is, anyone who adds functionality to open source software or
+wishes to redistribute it (legally) must provide the software
+accompanied by its source free of charge.\footnote{As specified in the
+  \hlink{GNU General License v.  2}{http://www.gnu.org/copyleft}
+  \url{http://www.gnu.org/copyleft}.}  If you find a bug in open
+source software and post a note to the appropriate mailing list, a
+solution you can use will likely be posted quickly by one of the
+thousands of people using the program all over the world.  Since you
+can see the source code, you might even be able to fix it yourself.
+In contrast, if something goes wrong with commercial software, you
+have to wait for the programmers at the company to fix it (and
+speaking with them is probably out of the question), and wait for a
+new version to be released.
+
+We find that Zelig makes students and colleagues more amenable to
+using R, since the startup costs are lower, and since the manual and
+software are relatively self-contained.  This manual even includes an
+appendix devoted to the basics of advanced R programming, although you
+will not need it to run most procedures in Zelig.  A large and growing
+fraction of the world's quantitative methodologists and statisticians
+are moving to R, and the base of programs available for R is quickly
+surpassing all alternatives.  In addition to built-in functions, R is
+a complete programming language, which allows you to design new
+functions to suit your needs.  R has the dual advantage that you do
+not need to understand how to program to use it, but if it turns out
+that you want to do something more complicated, you do not need to
+learn another program.  In addition, methodologists all over the world
+add new functions all the time, so if the function you need wasn't
+there yesterday, it may be available today.
+
+\section{Getting Help}
+
+You may find documentation for Zelig on-line (and hence must be
+on-line to access it).  If you are unable to connect to the Internet,
+we recommend that you print the pdf version of this document for your
+reference.
+
+If you are on-line, you may access comprehensive help files for Zelig
+commands and for each of the models.  For example, load the Zelig
+library and then type at the R prompt:
+\begin{verbatim}
+> help.zelig(command)                # For help with all zelig commands.
+> help.zelig(logit)                  # For help with the logit model.  
+\end{verbatim}
+\label{Rhelp}In addition, {\tt help.zelig()} searches the manual pages 
+for R in addition to the Zelig specific pages.  On certain rare
+occasions, the name of the help topic in Zelig and in R are identical.
+In these cases, {\tt help.zelig()} will return the Zelig help page by
+default.  If you wish to access the R help page, you should use {\tt
+  help(topic)}.
+
+In addition, built-in examples with sample data and plots are
+available for each model.  For example, type {\tt demo(logit)} to view
+the demo for the logit model.  Commented code for each model is
+available under the examples section of each model reference page.
+
+Please direct inquiries and problems about Zelig to our listserv at
+\hlink{zelig at latte.harvard.edu}{mailto:zelig at latte.harvard.edu}.  We
+suggest you subscribe to this mailing list while learning and using
+Zelig: go to \hlink{\url{http://lists.hmdc.harvard.edu/index.cgi?info=zelig}}{http://lists.hmdc.harvard.edu/index.cgi?info=zelig}.  (You can choose to receive email
+in digest form, so that you will never receive more than one message
+per day.)  You can also browse or search our
+\hlink{archive}{http://lists.hmdc.harvard.edu/lists/zelig/} of
+previous messages before posting your query.
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "zelig"
+%%% End: 
diff --git a/inst/doc/models/2sls.tex b/inst/doc/models/2sls.tex
new file mode 100644
index 0000000..e1611b9
--- /dev/null
+++ b/inst/doc/models/2sls.tex
@@ -0,0 +1,205 @@
+\documentclass[12pt]{book}%
+\usepackage{amsfonts}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{graphicx}
+\usepackage{fullpage}
+\usepackage{multirow}
+\usepackage{hyperref}%
+\usepackage{bibentry}
+\begin{document}
+\subsection{\texttt{2sls}: Two Stage Least Squares}
+\label{2sls}
+\texttt{2sls} provides consistent estimates for linear regression models with 
+some explanatory variable (the instrumental variable) 
+correlated with the error term. 
+In this situation, ordinary least squares fails to provide consistent 
+estimates. The name two-stage least squares stems from the two regressions 
+in the estimation procedure. In stage one, an ordinary least squares 
+prediction of the instrumental variable is obtained from regressing it on
+the instrument variables. In stage two, the coefficients of interst are 
+estimated using ordinary least square after substituting the instrumental 
+variable by its predictions from stage one. 
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> fml <- list ("mu"  = Y ~ X + Z,
+               "inst" = Z ~ W + X)
+> z.out <- zelig(formula = fml, model = "2sls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+\subsubsection{Inputs}
+\texttt{2sls} regression take the following inputs:
+\begin{itemize}
+\item \texttt{formula}:a list of the main equation and instrumental variable 
+equation. The first object in the list \texttt{mu} corresponds to the 
+regression model needs to be estimated. The second list object \texttt{inst} 
+specifies the regression model for the instrumental variable \texttt{Z}.
+For example:
+\begin{verbatim}
+> fml <- list ("mu"  = Y ~ X + Z,
+               "inst" = Z ~ W + X)
+\end{verbatim}
+\begin{itemize}
+\item \texttt{Y}: the dependent variable of interest.
+\item \texttt{Z}: the instrumental variable.
+\item \texttt{W}: exogenous instrument variables.
+\end{itemize}
+\end{itemize}
+\subsubsection{Additional Inputs}
+\texttt{2sls} takes the following additional inputs for model
+specifications:
+\begin{itemize}
+\item \texttt{TX}: an optional matrix to transform the regressor
+matrix and, hence, also the coefficient vector (see \ref{details}). Default is \texttt{NULL}.
+\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
+matrix (see \ref{details}). Default is equal to 1.
+\item \texttt{probdfsys}: use the degrees of freedom of the whole system
+(in place of the degrees of freedom of the single equation to calculate probability
+values for the t-test of individual parameters. 
+\item \texttt{single.eq.sigma}: use different $\sigma^2$ for each single
+equation to calculate the covariance matrix and the standard errors of the coefficients.
+\item \texttt{solvetol}: tolerance level for detecting linear dependencies when 
+inverting a matrix or calculating a determinant. Default is \texttt {solvetol}=.Machine\$double.eps.
+\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
+not crucial for the basic estimate (e.g McElroy's $R^2$).
+\end{itemize}
+\subsubsection{Details}
+\label{details}
+\begin{itemize}
+\item \texttt{TX}: The matrix \texttt{TX} transforms the regressor matrix 
+($X$) by $X\ast=X \times TX$. Thus,
+the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the 
+original(stacked) 
+vector of all coefficients and $b\ast$ is the new coefficient vector 
+that is estimated instead.
+Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
+used to change the order of the coefficients and also to restrict coefficients (if $TX$ has 
+less columns than it has rows).
+\item \texttt{rcovformula}: The formula to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
+of the following (see Judge et al., 1955, p.469):
+if \texttt{rcovformula}= 0:
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
+\end{eqnarray*}
+if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
+\end{eqnarray*}
+if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
+\end{eqnarray*}
+if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
+\end{eqnarray*}
+If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
+for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
+yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
+positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
+\end{itemize}
+\subsubsection{Examples}
+ Attaching the example dataset:
+ \begin{verbatim} 
+ > data(klein)
+ \end{verbatim}
+ Formula:
+\begin{verbatim}
+ >formula<- list(mu1=C~Wtot + P + P1,
+               mu2=I~P + P1 + K1,
+               mu3=Wp~ X + X1 + Tm,
+               inst= ~ P1 + K1 + X1 + Tm + Wg + G)
+\end{verbatim}
+Estimating the model using \texttt{2sls}:
+\begin{verbatim}
+> z.out<-zelig(formula=formula, model="2sls",data=klein)
+> summary(z.out)
+\end{verbatim}
+
+Set explanatory variables to their default (mean/mode) values
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+
+Simulate draws from the posterior distribution:
+\begin{verbatim}
+> s.out <- sim(z.out)
+> summary(s.out)
+\end{verbatim}
+\clearpage
+\subsubsection{Model}
+Let's consider the following regression model,
+\begin{eqnarray*}
+Y_i=X_i\beta + Z_i\gamma + \epsilon_i, \quad  i=1,\ldots,N
+\end{eqnarray*}
+where $Y_i$ is the dependent variable, 
+$X_i = (X_{1i},\ldots, X_{Ni})$ is the vector of explanatory variables, 
+$\beta$ is the vector of coefficients of the explanatory variables $X_i$, 
+$Z_i$ 
+is the problematic explanatory variable, and $\gamma$ is the coefficient
+ of $Z_i$.  In the equation, there is a direct dependence of $Z_i$ 
+on the structural disturbances of $\epsilon$.
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+\epsilon_i  &  \sim & {\cal N}(0, \sigma^2), \quad {\rm and} \quad
+{\rm cov}(Z_i, \epsilon_i) \ne 0,
+\end{eqnarray*}
+\item The \emph{systematic component} is given by:
+\begin{eqnarray*}
+\mu_{i}= E(Y_i)= X_{i}\beta + Z_i\gamma,
+\end{eqnarray*}
+\end{itemize}
+\noindent To correct the problem caused by the correlation of $Z_i$ and $\epsilon$, 
+two stage least squares utilizes two steps:
+\begin{itemize}
+\item \emph{Stage 1}: A new instrumental variable $\hat{Z}$ is created 
+for $Z_i$ which is the ordinary least squares predictions from regressing 
+$Z_i$ on a set of exogenous instruments $W$ and $X$.
+\begin{eqnarray*}
+\widehat{Z_i} = \widetilde{W}_i[(\widetilde{W}^\top\widetilde{W})^{-1}\widetilde{W}^\top Z]
+\end{eqnarray*}
+where $\widetilde{W} = (W,X)$
+\item \emph{Stage 2}: Substitute for $\hat{Z}_i$ for $Z_i$ in the original 
+equation, estimate $\beta$ and $\gamma$ by ordinary least squares regression
+of $Y$ on $X$ and $\hat{Z}$ as in the following equation. 
+\begin{eqnarray*}
+Y_i=X_i\beta + \widehat{Z_i}\gamma + \epsilon_i,  \quad {\rm for} 
+\quad   i=1,\ldots,N
+\end{eqnarray*}
+\end{itemize}
+\subsubsection{See Also}
+For information about three stage least square regression, see 
+\Sref{3sls} and \texttt{help(3sls)}.
+For information about seemingly unrelated regression, see
+\Sref{sur} and \texttt{help(sur)}.
+\subsubsection{Quantities of Interest}
+\subsubsection{Output Values}
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(formula=fml, model = "2sls", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below:
+\begin{itemize}
+\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
+\item \texttt{single.eq.sigma}: different $\sigma^2$s for each single equation?.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\end{itemize}
+
+\input{systemfit_output}
+
+\begin{itemize}
+\item \texttt{inst*}: instruments of the ith equation.
+\item \texttt{h*}: matrix of instrumental variables of the ith equation. 
+\end{itemize}
+\subsubsection{Contributors}
+The \texttt{2sls} function is adapted from \input{contributors_systemfit}
+\end{document}
+
diff --git a/inst/doc/models/3sls.tex b/inst/doc/models/3sls.tex
new file mode 100644
index 0000000..8495668
--- /dev/null
+++ b/inst/doc/models/3sls.tex
@@ -0,0 +1,181 @@
+\documentclass[12pt]{book}%
+\usepackage{amsfonts}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{graphicx}
+\usepackage{fullpage}
+\usepackage{multirow}
+\usepackage{hyperref}%
+\usepackage{bibentry}
+\begin{document}
+\subsection{\texttt{3sls}: Three Stage Least Squares}
+\label{3sls}
+\texttt{3sls} is a combination of two stage least squares
+and seemingly unrelated regression. It provides consistent estimates for linear regression models with 
+explanatory variables correlated with the error term. It also extends ordinary least squares 
+analysis to estimate system of linear equations with correlated error terms
+\subsubsection{Syntax}
+\begin{verbatim}
+> fml <- list ("mu1"  = Y1 ~ X1 + Z1,
+               "mu2"  = Y2 ~ X2 + Z2,
+               "inst1" = Z1 ~ W1 + X1,
+               "inst2" = Z2 ~ W2 + X2)
+> z.out <- zelig(formula = fml, model = "3sls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+\subsubsection{Inputs}
+\texttt{3sls} regression specification requires at least two sets of equations. The first set of $M$ euqations
+corresponds to the $M$ dependent variables ($Y_1,\ldots,Y_M$) to be estimated. The second set of equations ($Z$)
+corresponds to the instrumental variables in the $M$ equations.
+\begin{itemize}
+\item \texttt{formula}:a list of the system of equations and instrumental variable 
+equations. The system of equations is listed first as \texttt{mu}s. The equations
+for the instrumental variables are listed next as \texttt{inst}s.
+For example:
+\begin{verbatim}
+> fml <- list ("mu1"  = Y1 ~ X1 + Z1,
+               "mu2"  = Y2 ~ X2 + Z2,
+               "inst1" = Z1 ~ W1 + X1,
+               "inst2" = Z2 ~ W2 + X2)
+\end{verbatim}
+\texttt{"mu1"} is the first equation in the two equation model with \texttt{Y1}
+as the dependent variable and \texttt{X1} and \texttt{Z1} as
+the explanatory variables. \texttt{"mu2"} is the second equation with 
+\texttt{Y2} as the dependent variable
+and \texttt{X2} and \texttt{Z2} as the explanatory variables. 
+\texttt{Z1} and \texttt{Z2} are also problematic endogenous variables, so
+they are estimated through instruments in the \texttt{"inst1"} 
+and \texttt{"inst2"} equations.
+\item \texttt{Y}: dependent variables of interest in the system of equations.
+\item \texttt{Z}: the problematic explanatory variables correlated with 
+the error term.
+\item \texttt{W}: exogenous instrument variables used to estimate the 
+problematic explanatory variables (\texttt{Z})
+\end{itemize}
+\subsubsection{Additional Inputs}
+\texttt{3sls} takes the following additional inputs for model
+specifications:
+\begin{itemize}
+\item \texttt{TX}: an optional matrix to transform the regressor
+matrix and, hence, also the coefficient vector (see details). Default is \texttt{NULL}.
+\item \texttt{maxiter}: maximum number of iterations.
+\item \texttt{tol}: tolerance level indicating when to stop the iteration.
+\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
+matrix (see details). Default is equal to 1.
+\item \texttt{formula3sls}: formula for calculating the 3sls estimator, one of ``GLS'',
+``IV'', ``GMM'', ``Schmidt'', or ``Eviews'' (see details.)
+\item \texttt{probdfsys}: use the degrees of freedom of the whole system
+(in place of the degrees of freedom of the single equation to calculate probability
+values for the t-test of individual parameters. 
+\item \texttt{single.eq.sigma}: use different $\sigma^2$ for each single
+equation to calculate the covariance matrix and the standard errors of the coefficients.
+\item \texttt{solvetol}: tolerance level for detecting linear dependencies when 
+inverting a matrix or calculating a determinant. Default is \texttt {solvetol}=.Machine\$double.eps.
+\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
+not crucial for the basic estimate (e.g McElroy's $R^2$).
+\end{itemize}
+\subsubsection{Details}
+The matrix \texttt{TX} transforms the regressor matrix ($X$) by $X\ast=X \times TX$. Thus,
+the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the original(stacked) 
+vector of all coefficients and $b\ast$ is the new coefficient vector that is estimated instead.
+Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
+used to change the order of the coefficients and also to restrict coefficients (if $TX$ has 
+less columns than it has rows). 
+If iterated (with \texttt{maxit}>1), the covergence criterion is
+\begin{eqnarray*}
+\sqrt{\frac{\sum_i(b_{i,g}-b_{i,g-1})^2}{\sum_ib_{i,g-1}^2}} < tol
+\end{eqnarray*}
+where $b_{i,g}$ is the ith coefficient of the gth iteration step.
+The formula (\texttt{rcovformula} to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
+of the following (see Judge et al., 1955, p.469):
+if \texttt{rcovformula}= 0:
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
+\end{eqnarray*}
+if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
+\end{eqnarray*}
+if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
+\end{eqnarray*}
+if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
+\end{eqnarray*}
+If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
+for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
+yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
+positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
+(Theil, 1971, p.322).
+The formulas to calculate the 3sls estimator lead to identical results 
+if the same instruments are used in all equations. If different instruments 
+are used in the different equations, only the GMM-3sls estimator (``GMM'') 
+and the 3sls estimator proposed by Schmidt (1990) (``Schmidt'') are consistent, whereas 
+``GMM'' is efficient relative to ``Schmidt'' (see Schmidt, 1990).
+\subsubsection{Examples}
+Attaching the example dataset:
+ \begin{verbatim} 
+ > data(kmenta)
+ \end{verbatim}
+ Formula:
+\begin{verbatim}
+ >formula<-list(mu1= q ~ p + d,
+                mu2=q ~ p + f + a,
+                inst =~ d + f+ a )
+\end{verbatim}
+Estimating the model using \texttt{3sls}:
+\begin{verbatim}
+> z.out<-zelig(formula=formula, model ="3sls",data=kmenta)
+> summary(z.out)
+\end{verbatim}
+
+Set explanatory variables to their default (mean/mode) values
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+
+Simulate draws from the posterior distribution:
+\begin{verbatim}
+> s.out <- sim(z.out)
+> summary(s.out)
+\end{verbatim}
+\clearpage
+
+\subsubsection{Model}
+\subsubsection{See Also}
+For information about two stage least square regression, see 
+\Sref{2sls} and \texttt{help(2sls)}.
+For information about seemingly unrelated regression, see
+\Sref{sur} and \texttt{help(sur)}.
+\subsubsection{Quantities of Interest}
+\subsubsection{Output Values}
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(formula=fml, model = "3sls", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below:
+\begin{itemize}
+\item \texttt{rcovest}: residual covariance matrix used for estimation.
+\item \texttt{mcelr2}: McElroys R-squared value for the system.
+\item \texttt{h}: matrix of all (diagonally stacked) instrumental variables.
+\item \texttt{formula3sls}: formula for calculating the 3sls estimator 
+
+\end{itemize}
+\input{systemfit_output}
+\begin{itemize}
+\item \texttt{inst*}: instruments of the ith equation.
+\item \texttt{h*}: matrix of instrumental variables of the ith equation. 
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\end{itemize}
+\subsubsection{Contributors}
+The \texttt{3sls} function is adapted from \input{contributors_systemfit}
+\end{document}
+
diff --git a/inst/doc/models/arima.aux b/inst/doc/models/arima.aux
new file mode 100644
index 0000000..2477c21
--- /dev/null
+++ b/inst/doc/models/arima.aux
@@ -0,0 +1,43 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.1}{\tt  arima}: {\sc  arima} Models for Time Series Data}{123}{section.12.1}}
+\newlabel{arima}{{12.1}{123}{{\tt arima}: {\sc arima} Models for Time Series Data\relax }{section.12.1}{}}
+\citation{Enders04}
+\@writefile{toc}{\contentsline {paragraph}{Example 1: No External Regressors}{125}{section*.135}}
+\@writefile{toc}{\contentsline {paragraph}{Example 2: External Regressors, 1 Counterfactual, 1 Time Period}{125}{section*.136}}
+\citation{BroDav91}
+\@writefile{toc}{\contentsline {paragraph}{Example 3: External Regressors, Counterfactuals Over Many Time Periods}{126}{section*.137}}
+\citation{VenRip02}
+\citation{Enders04}
+\citation{BroDav91}
+\@setckpt{models/arima}{
+\setcounter{page}{129}
+\setcounter{equation}{0}
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+}
diff --git a/inst/doc/models/arima.tex b/inst/doc/models/arima.tex
new file mode 100644
index 0000000..ee28454
--- /dev/null
+++ b/inst/doc/models/arima.tex
@@ -0,0 +1,320 @@
+\section{{\tt arima}: {\sc arima} Models for Time Series Data}
+\label{arima} 
+Use {\sc arima} (autoregressive, integrated, moving average) to model
+time series data.  {\sc arima} models relax the independence assumption
+between observations, allowing for temporal dependence.  
+\subsubsection*{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Diff(Y, d, ds = NULL, per = NULL)~ lag.y(p, ps = NULL) 
+             + lag.eps(q, qs = NULL) + X1 + X2, 
+             model = "arima", data = mydata, ...)
+> x.out <- setx(z.out, X1 = list(time, value), cond = FALSE) 
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection*{Inputs}
+\begin{itemize}
+\item {\tt formula}: {\tt zelig()} uses special functions in the {\tt
+formula} to specify the {\sc arima} model: 
+\begin{itemize}
+\item {\tt Diff(Y, d, ds, per)}: for a dependent variable Y sets the
+number of non-seasonal differences ({\tt d}), the number of seasonal
+differences ({\tt ds}), and the period of the season ({\tt per}).
+
+\item {\tt lag.y(p, ps)}: sets the number of lagged observations of the
+dependent variable for non-seasonal ({\tt p}) and seasonal ({\tt ps})
+components.
+
+\item {\tt lag.eps(q, qs)}: sets the number of lagged innovations, or
+differences between the observed value of the time series and the
+expected value of the time series for non-seasonal ({\tt q}) and
+seaonal ({\tt qs}) components.  
+\end{itemize}
+
+\item $\hdots$: Additional inputs passed to {\tt arima()} in the {\tt
+stats} library. 
+\end{itemize}
+
+\subsubsection*{Additional Inputs to {\tt setx()}}
+
+The special characteristics of time-series data result in extra
+considerations for the other Zelig functions.  For the {\tt setx()}
+function:
+\begin{itemize}
+
+\item For external regressors {\tt X}, counterfactual values are
+specified as a list containing 
+
+\item {\tt pred.ahead}:  An integer value (defaulting to 0).  When no
+external covariates are used in the formula (e.g., the formula
+contains only the dependent variable ({\tt y}), lags to the dependent
+variable ({\tt lag.y()}), and lagged shocks ({\tt lag.eps()})), the
+user may predict future values in the time series by specifying the
+number of future values as an integer.  
+
+\item {\tt cond}: A logical value (defaulting to {\tt FALSE}).  
+When external regressors are used, {\tt cond = TRUE} computes the
+difference of the observed time series and the simulated values of the
+time series for a counterfactual value, which is the treatment effect
+of the counterfactual value.  You cannot select both a treatment
+effect and first differences simultaneously.  
+\end{itemize}
+
+\subsubsection*{Additional inputs to {\tt sim()}}
+
+The special characteristics of time-series data result in extra
+considerations for the other Zelig functions.  For the {\tt sim()}
+function:
+\begin{itemize}
+\item {\tt max.iter = 10}: An integer value (defaulting to 10)
+indicating the maximum number of times to draw parameters.  Time
+series coefficients must be stationary for the simulation procedure
+(see below).  Therefore, some simulated values of the coefficients
+will be discarded.  For models with coefficients that are very close
+to non-stationary, {\tt max.iter} should be increased.  
+\end{itemize}
+
+\subsubsection*{Additional inputs to {\tt plot()}}
+
+For the {\tt plot.zelig.arima} function:
+\begin{itemize}
+\item {\tt pred.se}: A logical value (defaults to {\tt FALSE})
+indicting whether the uncertainty from prediction in simulations
+should be computed along with the uncertainty from parameter
+simulation.
+ 
+\item {\tt color = c("blue", "red", "green3", "black")}:  A
+vector of four character strings identifying colors (see {\tt
+colors()} for a full list) to be used to plot the simulated
+values (blue), uncertainty from prediction (red), the counterfactual
+time period for a single counterfactual or the first counterfacutal
+for many counterfactual time periods and their associated prediction
+uncertainty (green3), and the original time series (black).  
+\end{itemize}
+
+\subsubsection*{Stationarity}
+Users should ensure that the time series being analyzed is
+stationary before specifying a model.  The following commands provide
+diagnostics to determine if a time series, $Y$, is stationary.  Due to
+the lack of power, these tests may provide contradictory results.  
+\begin{itemize}
+\item {\tt pp.test(Y)}: Tests the null hypothesis that the time series
+  is non-stationary.  
+\item {\tt kpss.test(Y)}: Tests the null hypothesis that the time
+  series model is stationary.
+\end{itemize}  
+The following commands provide graphical means of diagnosing whether a
+given time series is stationary.  
+\begin{itemize}
+\item {\tt ts.plot(Y)}: Plots the observed time series.  
+\item {\tt acf(Y)}: Provides the sample auto-correlation function
+(correlogram) for the time series.
+\item {\tt pacf(Y)}: Provides the sample partial auto-correlation
+  function (PACF) for the time series.  \\
+\end{itemize}
+These latter two plots are also useful in determining the
+number of autogressive terms and lagged innovations (size of ``p'' and
+``q'').  See \cite{Enders04} for details.  
+
+\subsubsection*{Examples}
+Load sample data 
+\begin{verbatim}
+> data(approval)
+\end{verbatim}
+\paragraph{Example 1: No External Regressors}
+Estimate the {\tt arima} model without any external explanatory
+variables, such that the right hand side of the {\tt formula} consists
+only of {\tt lag.eps()} and {\tt lag.y()} terms:
+\begin{verbatim}
+z.out1 <- zelig(Diff(approve, 1) ~ lag.eps(2) + lag.y(2), 
+              data = approval, model = "arima")
+summary(z.out1)
+\end{verbatim}
+Set the number of time periods beyond the end of the original series
+for which you would like the prediction to run:
+\begin{verbatim}
+x.out1 <- setx(z.out1, pred.ahead = 10)
+\end{verbatim}
+Simulate the predicted quantities of interest:
+\begin{verbatim}
+s.out1 <- sim(z.out1, x = x.out1)
+\end{verbatim}
+Summarize and plot the results:
+\begin{verbatim}
+summary(s.out1)
+plot(s.out1)
+\end{verbatim}
+
+\paragraph{Example 2: External Regressors, 1 Counterfactual, 1 Time Period}
+Estimate an {\sc arima} model that includes exogenous regressors
+in addition to lagged errors and lagged values of the dependent variable.  
+This example shows the output if only one counterfactual value is specified for
+a time period.  
+\begin{verbatim}
+z.out2 <- zelig(Diff(approve, 1) ~ iraq.war + sept.oct.2001 
+                + avg.price + lag.eps(1) + lag.y(2),
+                data = approval, model = "arima")
+\end{verbatim}
+Set the both the value and time period of counterfactual of interest.
+\begin{verbatim}
+x.out2 <- setx(z.out2, sept.oct.2001=list(time=45, 0))
+x1.out2 <- setx(z.out2, sept.oct.2001=list(time=45, 1))
+\end{verbatim}
+Simulate the quantities of interes
+\begin{verbatim}
+s.out2 <- sim(z.out2, x = x.out2, x1 = x1.out2) 
+\end{verbatim}
+Summarizing and plotting the quantities of interest.
+\begin{verbatim}
+summary(s.out2)
+plot(s.out2)
+\end{verbatim}
+
+\paragraph{Example 3: External Regressors, Counterfactuals Over Many Time Periods}
+This example continues to use the same model specification as above, but will show
+the output when several counterfactual values are specified. 
+\begin{verbatim}
+x.out3<- setx(z.out2, sept.oct.2001 = list(time = 45:50, 0))
+x1.out3<- setx(z.out2, sept.oct.2001 = list(time = 45:50, 1))
+\end{verbatim}
+Simulating the quantities of interest
+\begin{verbatim}
+s.out3 <- sim(z.out2, x = x.out3, x1 = x1.out3)
+\end{verbatim}
+Summarizing and plotting the quantities of interest.  The plot
+displays the uncertainty resulting from parameter estimation.  
+\begin{verbatim}
+summary(s.out3)
+plot(s.out3, pred.se = TRUE)
+\end{verbatim}
+
+\subsubsection*{Model}
+Suppose we obserive a time series $Y$, with observations $Y_t$ where
+$t$ denotes the time at which the observation was recorded.  Define
+the difference operator, $\nabla^d$ as follows.  When $d = 1$,
+$\nabla^{1} Y = Y_t - Y_{t-1}$, for all observations in the series.
+When $d=2$, $\nabla^2 Y = (Y_{t} - Y_{t-1}) - (Y_{t-1} - Y_{t-2})$,
+which notice is analagous to a polynomial expansion, $Y_{t} - 2Y_{t-1}
++ Y_{t-2}$.  Higher orders of differencing ($d > 2$) follow this same
+function.  Then $Y^*$ represents the stationary time series derived
+from the initial time series by differencing $Y$, $d$ times.  The
+model is estimated using the Kalman state-space representation of the
+specified model.  See \cite{BroDav91} for details.
+\begin{itemize}
+\item A stationary {\sc arima}$(p, d, q)$ time series at $t$, $Y_t^*$,
+has \textit{stochastic component} : 
+\begin{equation} \nonumber
+Y_t^* \sim \text{Normal} (\mu_t, \sigma^2) \nonumber,
+\end{equation}
+where $\mu_t$ and $\sigma^2$ are the mean and variance of the normal
+distribution, respectively.  
+\item The \textit{systematic component}, $\mu_t$ is modeled as
+\begin{equation*}
+\mu_t = x_t \beta + \alpha_1 Y_{t-1}^* + \hdots + \alpha_p Y_{t-p}^*
++ \gamma_1\epsilon_{t-1} + \hdots + \gamma_q \epsilon_{t-q}
+\end{equation*}
+where $x_t$ are the explanatory variables observed at time $t$, the
+$Y^*$'s are the lagged observations from the stationary time series,
+the $\epsilon$'s are the lagged innovations, the difference between
+the observed values of the time series and the expected values in each
+time period.  Finally, the $\beta$'s, $\alpha$'s, and $\gamma$'s
+represent parameters to be estimated.
+\end{itemize}
+
+\subsubsection*{Quantities of Interest}
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+  from the stochastic component, 
+\begin{equation*}
+\text{E(}Y_\text{i}) = \mu_i = x_i \beta + \alpha_1 Y_{i-1}^* + \hdots
++ \alpha_p Y_{i-p}^* + \gamma_1\epsilon_{i-1} + \hdots + \gamma_q \epsilon_{i-q}
+\end{equation*}
+given draws of $\beta$, $\alpha$, and $\gamma$ from their estimated
+distribution.
+\item The first difference ({\tt qi\$fd}) is: 
+\begin{equation*}
+\text{FD}_i= E(Y | x_{1i}) - E(Y|x_{i}) 
+\end{equation*}
+\item The treatment effect ({\tt qi\$t.eff}) represents the difference
+between the observed time series and the expected value of a time
+series with counterfacual values of the external regressors.  Formally,
+\begin{equation*}
+\text{t.eff}_\text{i} = Y_i - E[Y_i | x_{i}] 
+\end{equation*}
+Set {\tt cond = TRUE} to obtain this quantity of interest.  Note zelig
+will not estimate both first differences and treatment effects.
+\end{itemize}
+
+\subsubsection*{Output Values}
+The output of each Zelig command contains useful information which the
+user may view.  For example, if the user runs {\tt z.out <- zelig(
+Diff(Y,1) + lag.y(1) + lag.eps(1) + X1, model= "arima", data)} then
+the user may examine the available information in {\tt z.out} by using
+{\tt names(z.out)}, see the coefficients by using {\tt z.out\$coef}
+and a default summary of information through {\tt summary(z.out)}.
+{\tt tsdiag(z.out)} returns a plot of the residuals, the ACF of the
+residuals, and a plot displaying the p-values for the Ljung-Box
+statistic.  Other elements, available through the \$ operator are
+listed below.
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract: 
+\begin{itemize}
+\item {\tt coef}: parameter estimates for the explanatory variables,
+lagged observations of the time series, and lagged innovations.  
+\item {\tt sigma2} : maximum likelihood estimate of the variance of
+the stationary time series.
+\item {\tt var.coef} : variance-covariance matrix for the parameters.
+\item {\tt loglik} : maximized log-likelihood.
+\item {\tt aic} : Akaike Information Criterion (AIC) for the maximized
+  log-likelihood.
+\item {\tt residuals} : Residuals from the fitted model.
+\item {\tt arma}: A 7-element long vector displaying the AR, MA, seasonal AR and MA,
+period of the seasonal component, and the number of non-seasonal and
+seasonal differences of the dependent variable.  
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\end{itemize}
+\item From the {\tt sim()} output object {\tt s.out} you may extract
+  quantities of interest arranged as matrices, with the rows
+indicating the number of the simulations, and the columns
+representing the simulated value of the dependent variable for the
+counterfactual value at that time period.  {\tt summary(s.out)} provides a summary of the simulated
+values, while {\tt plot(s.out)} provides a graphical representation of
+the simulations.  
+  Available quantities are:
+\begin{itemize}
+\item {\tt qi\$ev}: the simulated expected probabilities for the
+  specified values of {\tt x}.  
+\item {\tt qi\$fd} : the simulated first difference for the values
+  that are specified in {\tt x} and {\tt x1}.
+\item{ \tt qi\$t.eff} : the simulated treatment effect, difference
+between y and the values specified in x.     
+\end{itemize}  
+\end{itemize}
+
+\subsubsection*{Contributors}
+Justin Grimmer added Zelig functionality.  The {\sc arima} model is
+part of the stats package by William N. Venables and Brian D. Ripley.
+Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+For an accesible introduction to identifying the order of an {\sc
+arima} model consult:
+\begin{verse}
+\bibentry{Enders04}.
+\end{verse} 
+
+Advanced users may wish to become more familiar with the state-space
+representation of an {\sc arima} process:
+\begin{verse}
+\bibentry{BroDav91}.
+\end{verse}
+
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "~/research/zelig/Zelig/inst/doc/zelig"
+%%% End: 
diff --git a/inst/doc/models/blogit.aux b/inst/doc/models/blogit.aux
new file mode 100644
index 0000000..37eff9d
--- /dev/null
+++ b/inst/doc/models/blogit.aux
@@ -0,0 +1,47 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.2}{\tt  blogit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{129}{section.12.2}}
+\newlabel{blogit}{{12.2}{129}{{\tt blogit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables\relax }{section.12.2}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{129}{section*.142}}
+\@writefile{toc}{\contentsline {subsubsection}{Input Values}{129}{section*.143}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{129}{section*.144}}
+\newlabel{basic.bl}{{1}{129}{Examples\relax }{Item.214}{}}
+\newlabel{sto.dep.logit}{{2}{130}{Examples\relax }{Item.215}{}}
+\newlabel{pdep.l}{{3}{131}{Examples\relax }{Item.216}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{131}{section*.145}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{132}{section*.146}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{133}{section*.147}}
+\citation{YeeHas03}
+\citation{Martin92}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{134}{section*.148}}
+\@setckpt{models/blogit}{
+\setcounter{page}{135}
+\setcounter{equation}{0}
+\setcounter{enumi}{3}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{2}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{216}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/blogit.tex b/inst/doc/models/blogit.tex
new file mode 100644
index 0000000..8f4a71b
--- /dev/null
+++ b/inst/doc/models/blogit.tex
@@ -0,0 +1,326 @@
+\section{{\tt blogit}: Bivariate Logistic Regression for Two
+Dichotomous Dependent Variables}\label{blogit}
+
+Use the bivariate logistic regression model if you have two binary
+dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
+function of some explanatory variables.  Each pair of dependent
+variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
+Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
+$(Y_{i1}=0, Y_{i2}=0)$.  The joint probability for each of these four
+outcomes is modeled with three systematic components: the marginal
+Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the odds ratio $\psi$,
+which describes the dependence of one marginal on the other.  Each of
+these systematic components may be modeled as functions of (possibly
+different) sets of explanatory variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2 , 
+                      mu2 = Y2 ~ X1 + X3), 
+                 model = "blogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+In every bivariate logit specification, there are three equations which
+correspond to each dependent variable ($Y_1$, $Y_2$), and $\psi$, the
+odds ratio. You should provide a list of formulas for each equation or, 
+you may use {\tt cbind()} if the right hand side is the same for both equations
+\begin{verbatim}
+formulae <- list(cbind(Y1,Y2) ~ X1 + X2)
+\end{verbatim}
+which means that all the explanatory variables in equations 1 and 2
+(corresponding to $Y_1$ and $Y_2$) are included, but only an intercept
+is estimated (all explanatory variables are omitted) for equation 3
+($\psi$).  
+
+You may use the function {\tt tag()} to constrain variables across
+equations:
+\begin{verbatim}
+formulae <- list(mu1 = y1 ~ x1 + tag(x3, "x3"), 
+                 mu2 = y2 ~ x2 + tag(x3, "x3"))
+\end{verbatim}
+where {\tt tag()} is a special function that constrains variables to
+have the same effect across equations.  Thus, the coefficient for {\tt
+x3} in equation {\tt mu1} is constrained to be equal to the
+coefficient for {\tt x3} in equation {\tt mu2}.  
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item {Basic Example} \label{basic.bl}
+
+Load the data and estimate the model:  
+\begin{verbatim}
+> data(sanction)
+> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target, 
+                  model = "blogit", data = sanction)
+\end{verbatim}
+By default, {\tt zelig()} estimates two effect parameters
+for each explanatory variable in addition to the odds ratio parameter;
+this formulation is parametrically independent (estimating
+unconstrained effects for each explanatory variable), but
+stochastically dependent because the models share an odds ratio.
+\newline \newline Generate baseline values for the explanatory
+variables (with cost set to 1, net gain to sender) and alternative
+values (with cost set to 4, major loss to sender):
+\begin{verbatim}
+> x.low <- setx(z.out1, cost = 1)
+> x.high <- setx(z.out1, cost = 4)
+\end{verbatim}
+Simulate fitted values and first differences:  
+\begin{verbatim}
+> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+> summary(s.out1)
+> plot(s.out1)
+\end{verbatim}
+
+\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.logit}
+
+Load the sample data:
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimate the statistical model, with {\tt import} a function of {\tt
+  coop} in the first equation and {\tt export} a function of {\tt
+  cost} and {\tt target} in the second equation:
+\begin{verbatim}
+> z.out2 <- zelig(list(import ~ coop, export ~ cost + target), 
+                  model = "blogit", data = sanction)
+> summary(z.out2)
+\end{verbatim}
+Set the explanatory variables to their means:
+\begin{verbatim}
+> x.out2 <- setx(z.out2)
+\end{verbatim}
+Simulate draws from the posterior distribution:
+\begin{verbatim}
+> s.out2 <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+> plot(s.out2)
+\end{verbatim}
+
+\item Joint Estimation of a Parametrically and Stochastically
+Dependent Model 
+\label{pdep.l}
+  
+Load the sample data:  
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+A bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
+some or all explanatory variables, {\it and} the effects of the shared
+explanatory variables are jointly estimated.  For example,
+\begin{verbatim}
+> z.out3 <- zelig(list(import ~ tag(coop,"coop") + tag(cost,"cost") + 
+                           tag(target,"target"), 
+                       export ~ tag(coop,"coop") + tag(cost,"cost") + 
+                           tag(target,"target")), 
+                       model = "blogit", data = sanction)
+> summary(z.out3)
+\end{verbatim}
+Note that this model only returns one parameter estimate for each of
+{\tt coop}, {\tt cost}, and {\tt target}.  Contrast this to
+Example~\ref{basic.bl} which returns two parameter estimates for each
+of the explanatory variables.  \newline \newline Set values for the
+explanatory variables:
+\begin{verbatim}
+> x.out3 <- setx(z.out3, cost = 1:4)
+\end{verbatim} 
+Draw simulated expected values:  
+\begin{verbatim}
+> s.out3 <- sim(z.out3, x = x.out3)
+> summary(s.out3)
+\end{verbatim}
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+For each observation, define two binary dependent variables, $Y_1$ and
+$Y_2$, each of which take the value of either 0 or 1 (in the
+following, we suppress the observation index).  We model the joint
+outcome $(Y_1$, $Y_2)$ using a marginal probability for each dependent
+variable, and the odds ratio, which parameterizes the relationship
+between the two dependent variables. Define $Y_{rs}$ such that it is
+equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise, where $r$ and
+$s$ take a value of either 0 or 1. Then, the model is defined as follows,
+
+\begin{itemize}
+ 
+\item The \emph{stochastic component} is
+\begin{eqnarray*}
+  Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
+  Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
+  Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
+\end{eqnarray*}
+where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
+$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$.
+
+
+\item The \emph{systematic components} model the marginal probabilities,
+  $\pi_j=\Pr(Y_j=1)$, as well as the odds ratio.  The odds ratio
+  is defined as $\psi = \pi_{00} \pi_{01}/\pi_{10}\pi_{11}$ and
+  describes the relationship between the two outcomes.  Thus, for each
+  observation we have
+\begin{eqnarray*}
+\pi_j & = & \frac{1}{1 + \exp(-x_j \beta_j)} \quad \textrm{ for} \quad
+j=1,2, \\
+\psi &= & \exp(x_3 \beta_3).
+\end{eqnarray*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the bivariate logit model
+  are the predicted joint probabilities. Simulations of $\beta_1$,
+  $\beta_2$, and $\beta_3$ (drawn from their sampling distributions)
+  are substituted into the systematic components $(\pi_1, \pi_2,
+  \psi)$ to find simulations of the predicted joint probabilities:
+\begin{eqnarray*}
+\pi_{11} & = & \left\{ \begin{array}{ll}
+                 \frac{1}{2}(\psi - 1)^{-1} - {a - \sqrt{a^2 + b}} &
+                 \textrm{for} \; \psi \ne 1 \\
+                 \pi_1 \pi_2 & \textrm{for} \; \psi = 1 
+                 \end{array} \right., \\
+\pi_{10} &=& \pi_1 - \pi_{11}, \\
+\pi_{01} &=& \pi_2 - \pi_{11}, \\
+\pi_{00} &=& 1 - \pi_{10} - \pi_{01} - \pi_{11},
+\end{eqnarray*}
+where $a = 1 + (\pi_1 + \pi_2)(\psi - 1)$, $b = -4 \psi(\psi - 1)
+\pi_1 \pi_2$, and the joint probabilities for each observation must sum
+to one.  For $n$ simulations, the expected values form an $n \times 4$
+matrix for each observation in {\tt x}.  
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+  multinomial distribution given the expected joint probabilities. 
+
+\item The first differences ({\tt qi\$fd}) for each
+  of the predicted joint probabilities are given by $$\textrm{FD}_{rs}
+  = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s \mid x).$$  
+  
+\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
+  probabilities are given by
+\begin{equation*}
+\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+      E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_{ij}(t_i=0)]$,
+    the counterfactual expected value of $Y_{ij}$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+      \widehat{Y_{ij}(t_i=0)} \right\} \textrm{ for } j = 1,2,
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating
+    $\widehat{Y_{ij}(t_i=0)}$, the counterfactual predicted value of
+    $Y_{ij}$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "blogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+obtain a default summary of information through {\tt summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: the named vector of coefficients.   
+   \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
+     fitted values.
+   \item {\tt predictors}: an $n \times 3$ matrix of the linear
+     predictors $x_j \beta_j$.
+   \item {\tt residuals}: an $n \times 3$ matrix of the residuals.  
+   \item {\tt df.residual}: the residual degrees of freedom.  
+   \item {\tt df.total}: the total degrees of freedom.
+   \item {\tt rss}: the residual sum of squares.  
+   \item {\tt y}: an $n \times 2$ matrix of the dependent variables.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+  \begin{itemize}
+  \item {\tt coef3}: a table of the coefficients with their associated
+    standard errors and $t$-statistics.
+  \item {\tt cov.unscaled}: the variance-covariance matrix. 
+  \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.  
+  \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as arrays indexed by simulation
+  $\times$ quantity $\times$ {\tt x}-observation (for more than one
+  {\tt x}-observation; otherwise the quantities are matrices).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected joint probabilities (or expected
+     values) for the specified values of {\tt x}.  
+   \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
+     distribution defined by the expected joint probabilities.
+   \item {\tt qi\$fd}: the simulated first difference in the
+     expected joint probabilities for the values specified in {\tt x} and
+     {\tt x1}.
+   \item {\tt qi\$rr}: the simulated risk ratio in the predicted
+     probabilities for given {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The bivariate logit function is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)} 
+in the VGAM library.  Additional
+documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
diff --git a/inst/doc/models/bprobit.aux b/inst/doc/models/bprobit.aux
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+++ b/inst/doc/models/bprobit.aux
@@ -0,0 +1,47 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.3}{\tt  bprobit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{135}{section.12.3}}
+\newlabel{bprobit}{{12.3}{135}{{\tt bprobit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables\relax }{section.12.3}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{135}{section*.149}}
+\@writefile{toc}{\contentsline {subsubsection}{Input Values}{135}{section*.150}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{136}{section*.151}}
+\newlabel{basic.bp}{{1}{136}{Examples\relax }{Item.217}{}}
+\newlabel{sto.dep.probit}{{2}{136}{Examples\relax }{Item.218}{}}
+\newlabel{pdep.p}{{3}{137}{Examples\relax }{Item.219}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{137}{section*.152}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{138}{section*.153}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{139}{section*.154}}
+\citation{YeeHas03}
+\citation{Martin92}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{140}{section*.155}}
+\@setckpt{models/bprobit}{
+\setcounter{page}{142}
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+\setcounter{chapter}{12}
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diff --git a/inst/doc/models/bprobit.tex b/inst/doc/models/bprobit.tex
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--- /dev/null
+++ b/inst/doc/models/bprobit.tex
@@ -0,0 +1,357 @@
+\section{{\tt bprobit}: Bivariate Logistic Regression for Two
+Dichotomous Dependent Variables}\label{bprobit}
+
+Use the bivariate probit regression model if you have two binary
+dependent variables $(Y_1, Y_2)$, and wish to model them jointly as a
+function of some explanatory variables.  Each pair of dependent
+variables $(Y_{i1}, Y_{i2})$ has four potential outcomes, $(Y_{i1}=1,
+Y_{i2}=1)$, $(Y_{i1}=1, Y_{i2}=0)$, $(Y_{i1}=0, Y_{i2}=1)$, and
+$(Y_{i1}=0, Y_{i2}=0)$.  The joint probability for each of these four
+outcomes is modeled with three systematic components: the marginal
+Pr$(Y_{i1} = 1)$ and Pr$(Y_{i2} = 1)$, and the correlation parameter
+$\rho$ for the two marginal distributions.  Each of these systematic
+components may be modeled as functions of (possibly different) sets of
+explanatory variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(list(mu1 = Y1 ~ X1 + X2, 
+                      mu2 = Y2 ~ X1 + X3,
+                      rho = ~ 1),
+                 model = "bprobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+In every bivariate probit specification, there are three equations
+which correspond to each dependent variable ($Y_1$, $Y_2$), and the
+correlation parameter $\rho$.  Since the correlation parameter does
+not correspond to one of the dependent variables, the model estimates
+$\rho$ as a constant by default.  Hence, only two formulas (for
+$\mu_1$ and $\mu_2$) are required.  If the explanatory variables for
+$\mu_1$ and $\mu_2$ are the same and effects are estimated separately
+for each parameter, you may use the following short hand:  
+\begin{verbatim}
+fml <- list(cbind(Y1,Y2) ~ X1 + X2)
+\end{verbatim}
+which has the same meaning as:  
+\begin{verbatim}
+fml <- list(mu1 = Y1 ~ X1 + X2,  
+            mu2 = Y2 ~ X1 + X2, 
+            rho = ~ 1)
+\end{verbatim}
+You may use the function {\tt tag()} to constrain variables across
+equations.  The {\tt tag()} function takes a variable and a label for
+the effect parameter.  Below, the constrained effect of {\tt
+x3} in both equations is called the {\tt age} parameter:  
+\begin{verbatim}
+fml <- list(mu1 = y1 ~ x1 + tag(x3, "age"), 
+            mu2 = y2 ~ x2 + tag(x3, "age"))
+\end{verbatim}
+You may also constrain different variables across different equations
+to have the same effect.  
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+
+\item {Basic Example} \label{basic.bp}
+
+Load the data and estimate the model:  
+\begin{verbatim}
+> data(sanction)
+> z.out1 <- zelig(cbind(import, export) ~ coop + cost + target, 
+                  model = "bprobit", data = sanction)
+\end{verbatim}
+By default, {\tt zelig()} estimates two effect parameters
+for each explanatory variable in addition to the correlation coefficient;
+this formulation is parametrically independent (estimating
+unconstrained effects for each explanatory variable), but
+stochastically dependent because the models share a correlation parameter.
+\newline \newline Generate baseline values for the explanatory
+variables (with cost set to 1, net gain to sender) and alternative
+values (with cost set to 4, major loss to sender):
+\begin{verbatim}
+> x.low <- setx(z.out1, cost = 1)
+> x.high <- setx(z.out1, cost = 4)
+\end{verbatim}
+Simulate fitted values and first differences:  
+\begin{verbatim}
+> s.out1 <- sim(z.out1, x = x.low, x1 = x.high)
+> summary(s.out1)
+> plot(s.out1)
+\end{verbatim}
+
+\item {Joint Estimation of a Model with Different Sets of Explanatory Variables}\label{sto.dep.probit}
+
+Load the sample data:
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimate the statistical model, with {\tt import} a function of {\tt
+  coop} in the first equation and {\tt export} a function of {\tt
+  cost} and {\tt target} in the second equation:
+\begin{verbatim}
+> fml2 <- list(mu1 = import ~ coop, 
+               mu2 = export ~ cost + target)
+> z.out2 <- zelig(fml2, model = "bprobit", data = sanction)
+> summary(z.out2)
+\end{verbatim}
+Set the explanatory variables to their means:
+\begin{verbatim}
+> x.out2 <- setx(z.out2)
+\end{verbatim}
+Simulate draws from the posterior distribution:
+\begin{verbatim}
+> s.out2 <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+> plot(s.out2)
+\end{verbatim}
+
+\item Joint Estimation of a Parametrically and Stochastically
+Dependent Model 
+\label{pdep.p}
+  
+Load the sample data:  
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+A bivariate model is parametrically dependent if $Y_1$ and $Y_2$ share
+some or all explanatory variables, {\it and} the effects of the shared
+explanatory variables are jointly estimated.  For example,
+\begin{verbatim}
+> fml3 <- list(mu1 = import ~ tag(coop,"coop") + tag(cost,"cost") + 
+                          tag(target,"target"), 
+               mu2 = export ~ tag(coop,"coop") + tag(cost,"cost") + 
+                          tag(target,"target"))
+> z.out3 <- zelig(fml3, model = "bprobit", data = sanction)
+> summary(z.out3)
+\end{verbatim}
+Note that this model only returns one parameter estimate for each of
+{\tt coop}, {\tt cost}, and {\tt target}.  Contrast this to
+Example~\ref{basic.bp} which returns two parameter estimates for each
+of the explanatory variables.  \newline \newline Set values for the
+explanatory variables:
+\begin{verbatim}
+> x.out3 <- setx(z.out3, cost = 1:4)
+\end{verbatim} 
+Draw simulated expected values:  
+\begin{verbatim}
+> s.out3 <- sim(z.out3, x = x.out3)
+> summary(s.out3)
+\end{verbatim}
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+For each observation, define two binary dependent variables, $Y_1$ and
+$Y_2$, each of which take the value of either 0 or 1 (in the
+following, we suppress the observation index $i$).  We model the joint
+outcome $(Y_1$, $Y_2)$ using two marginal probabilities for each
+dependent variable, and the correlation parameter, which describes how
+the two dependent variables are related. 
+%Define $Y_{rs}$ such that it
+%is equal to 1 when $Y_1=r$ and $Y_2=s$ and is 0 otherwise where $r$
+%and $s$ take a value of either 0 or 1. Then, the model is defined as
+%follows,
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by two latent (unobserved)
+  continuous variables which follow the bivariate Normal distribution:
+\begin{eqnarray*}
+  \left ( \begin{array}{c} 
+      Y_1^* \\
+      Y_2^* 
+    \end{array}
+  \right ) &\sim &  
+  N_2 \left \{ \left ( 
+      \begin{array}{c}
+        \mu_1 \\ \mu_2
+      \end{array} \right ), \left( \begin{array}{cc}
+                 1 & \rho \\
+                 \rho & 1 
+                 \end{array} \right) \right\},
+\end{eqnarray*}
+where $\mu_j$ is a mean for $Y_j^*$ and $\rho$ is a scalar correlation
+parameter. The following observation mechanism links the observed
+dependent variables, $Y_j$, with these latent variables
+\begin{eqnarray*}
+Y_j & = & \left \{ \begin{array}{cc}
+                   1 & {\rm if} \; Y_j^* \ge 0, \\
+                   0 & {\rm otherwise.}
+                   \end{array} 
+                   \right.
+\end{eqnarray*}
+
+%Alternatively, the stochastic component for the observed dependent
+%variables can be written as
+%\begin{eqnarray*}
+%  Y_{11} &\sim& \textrm{Bernoulli}(y_{11} \mid \pi_{11}) \\
+%  Y_{10} &\sim& \textrm{Bernoulli}(y_{10} \mid \pi_{10}) \\
+% Y_{01} &\sim& \textrm{Bernoulli}(y_{01} \mid \pi_{01})
+%\end{eqnarray*}
+%where $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$ is the joint probability, and
+%$\pi_{00}=1-\pi_{11}-\pi_{10}-\pi_{01}$. Each of these joint
+%probabilities is modeled using the bivariate normal cumulative
+%distribution function.
+
+\item The \emph{systemic components} for each observation are 
+  \begin{eqnarray*}
+    \mu_j & = & x_{j} \beta_j \quad {\rm for} \quad j=1,2, \\
+    \rho & = & \frac{\exp(x_3 \beta_3) - 1}{\exp(x_3 \beta_3) + 1}.
+\end{eqnarray*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+For $n$ simulations, expected values form an $n \times 4$
+matrix.  
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the binomial probit model
+  are the predicted joint probabilities. Simulations of $\beta_1$,
+  $\beta_2$, and $\beta_3$ (drawn form their sampling distributions)
+  are substituted into the systematic components, to find simulations
+  of the predicted joint probabilities $\pi_{rs}=\Pr(Y_1=r, Y_2=s)$:
+\begin{eqnarray*}
+\pi_{11} &= \Pr(Y_1^* \geq 0 , Y_2^* \geq 0) &= \int_0^{\infty}
+\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^* \\
+\pi_{10} &= \Pr(Y_1^* \geq 0 , Y_2^* < 0)  &= \int_0^{\infty}
+\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\pi_{01} &= \Pr(Y_1^* < 0 , Y_2^* \geq 0)  &= \int_{-\infty}^{0}
+\int_0^{\infty} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\pi_{11} &= \Pr(Y_1^* < 0 , Y_2^* < 0)  &= \int_{-\infty}^{0}
+\int_{-\infty}^{0} \phi_2 (\mu_1, \mu_2, \rho) \, dY_2^*\, dY_1^*\\
+\end{eqnarray*}
+where $r$ and $s$ may take a value of either 0 or 1, $\phi_2$ is the
+bivariate Normal density.
+  
+\item The predicted values ({\tt qi\$pr}) are draws from the
+  multinomial distribution given the expected joint probabilities.  
+
+\item The first difference ({\tt qi\$fd}) in each of the predicted joint
+  probabilities are given by
+  $$\textrm{FD}_{rs} = \Pr(Y_1=r, Y_2=s \mid x_1)-\Pr(Y_1=r, Y_2=s
+  \mid x).$$
+  
+\item The risk ratio ({\tt qi\$rr}) for each of the predicted joint
+  probabilities are given by
+\begin{equation*}
+\textrm{RR}_{rs} = \frac{\Pr(Y_1=r, Y_2=s \mid x_1)}{\Pr(Y_1=r, Y_2=s \mid x)}.
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+      E[Y_{ij}(t_i=0)] \right\} \textrm{ for } j = 1,2,
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_{ij}(t_i=0)]$,
+    the counterfactual expected value of $Y_{ij}$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_{ij}(t_i=1) -
+      \widehat{Y_{ij}(t_i=0)}\right\} \textrm{ for } j = 1,2,
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating
+    $\widehat{Y_{ij}(t_i=0)}$, the counterfactual predicted value of
+    $Y_{ij}$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\, x,
+  model = "bprobit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+obtain a default summary of information through
+\texttt{summary(z.out)}.  Other elements available through the {\tt
+  \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: the named vector of coefficients.   
+   \item {\tt fitted.values}: an $n \times 4$ matrix of the in-sample
+     fitted values.
+   \item {\tt predictors}: an $n \times 3$ matrix of the linear
+     predictors $x_j \beta_j$.
+   \item {\tt residuals}: an $n \times 3$ matrix of the residuals.  
+   \item {\tt df.residual}: the residual degrees of freedom.  
+   \item {\tt df.total}: the total degrees of freedom.
+   \item {\tt rss}: the residual sum of squares.  
+   \item {\tt y}: an $n \times 2$ matrix of the dependent variables.  
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+  \item {\tt coef3}: a table of the coefficients with their associated
+    standard errors and $t$-statistics.
+  \item {\tt cov.unscaled}: the variance-covariance matrix. 
+  \item {\tt pearson.resid}: an $n \times 3$ matrix of the Pearson residuals.  
+\end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as arrays indexed by simulation
+  $\times$ quantity $\times$ {\tt x}-observation (for more than one
+  {\tt x}-observation; otherwise the quantities are matrices).  Available quantities
+  are:  
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values (joint predicted
+     probabilities) for the specified values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted outcomes drawn from a
+     distribution defined by the joint predicted probabilities.
+   \item {\tt qi\$fd}: the simulated first difference in the predicted
+     probabilities for the values specified in {\tt x} and {\tt x1}.
+   \item {\tt qi\$rr}: the simulated risk ratio in the predicted
+     probabilities for given {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The bivariate probit function is part of the VGAM package by Thomas Yee.
+Please cite this model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM package.  Additional documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from 
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
diff --git a/inst/doc/models/coda_diag.tex b/inst/doc/models/coda_diag.tex
new file mode 100644
index 0000000..c915940
--- /dev/null
+++ b/inst/doc/models/coda_diag.tex
@@ -0,0 +1,32 @@
+\subsubsection{Convergence}
+
+Users should verify that the Markov Chain converges to its stationary 
+distribution.  After running the \texttt{zelig()} function but before 
+performing \texttt{setx()}, users may conduct the following 
+convergence diagnostics tests:
+
+\begin{itemize}
+\item \texttt{geweke.diag(z.out\$coefficients)}: The Geweke diagnostic tests
+the null hypothesis that the Markov chain is in the stationary distribution
+and produces z-statistics for each estimated parameter. 
+ 
+\item \texttt{heidel.diag(z.out\$coefficients)}: The Heidelberger-Welch
+diagnostic first tests the null hypothesis that the Markov Chain is in the
+stationary distribution and produces p-values for each estimated parameter. 
+ Calling \texttt{heidel.diag()} also produces
+output that indicates whether the mean of a marginal posterior distribution
+can be estimated with sufficient precision, assuming that the Markov Chain is
+in the stationary distribution.
+
+\item \texttt{raftery.diag(z.out\$coefficients)}: The Raftery diagnostic
+indicates how long the Markov Chain should run before considering draws from
+the marginal posterior distributions sufficiently representative of the
+stationary distribution.
+\end{itemize}
+
+\noindent If there is evidence of non-convergence, adjust the values 
+for \texttt{burnin} and \texttt{mcmc} and rerun \texttt{zelig()}.
+
+Advanced users may wish to refer to \texttt{help(geweke.diag)}, 
+\texttt{help(heidel.diag)}, and \texttt{help(raftery.diag)} for more 
+information about these diagnostics. 
diff --git a/inst/doc/models/contributors.tex b/inst/doc/models/contributors.tex
new file mode 100644
index 0000000..c2830e4
--- /dev/null
+++ b/inst/doc/models/contributors.tex
@@ -0,0 +1,15 @@
+function is part of the MCMCpack library by Andrew D.
+Martin and Kevin M. Quinn. If you use this model, please cite:
+\begin{verse}
+\bibentry{MarQui05}.
+\end{verse}
+The convergence diagnostics are part of the CODA library
+by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
+should be cited as: 
+\begin{verse}
+\bibentry{PluBesCowVin05}.
+\end{verse}
+\noindent Sample data are adapted from 
+\begin{verse}
+\bibentry{MarQui05}.
+\end{verse}
diff --git a/inst/doc/models/contributors2.tex b/inst/doc/models/contributors2.tex
new file mode 100644
index 0000000..0958836
--- /dev/null
+++ b/inst/doc/models/contributors2.tex
@@ -0,0 +1,11 @@
+function is part of the MCMCpack library by Andrew D.
+Martin and Kevin M. Quinn. If you use this model, please cite:
+\begin{verse}
+\bibentry{MarQui05}
+\end{verse}
+The convergence diagnostics are part of the CODA library
+by Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. These diagnostics
+should be cited as: 
+\begin{verse}
+\bibentry{PluBesCowVin05}
+\end{verse}
diff --git a/inst/doc/models/contributors_systemfit.tex b/inst/doc/models/contributors_systemfit.tex
new file mode 100644
index 0000000..90a143a
--- /dev/null
+++ b/inst/doc/models/contributors_systemfit.tex
@@ -0,0 +1,4 @@
+The \texttt{systemfit} library by Jeff Hamann and Arne Henningsen. I fyou use this model, please cite:
+\begin{verse}
+\bibentry{HamaHenn05}
+\end{verse}
diff --git a/inst/doc/models/ei.dynamic.aux b/inst/doc/models/ei.dynamic.aux
new file mode 100644
index 0000000..85aee13
--- /dev/null
+++ b/inst/doc/models/ei.dynamic.aux
@@ -0,0 +1,47 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.4}\texttt  {ei.dynamic}: Quinn's Dynamic Ecological Inference Model}{142}{section.12.4}}
+\newlabel{ei.dynamic}{{12.4}{142}{\texttt {ei.dynamic}: Quinn's Dynamic Ecological Inference Model\relax }{section.12.4}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{142}{section*.156}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{142}{section*.157}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{142}{section*.158}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{143}{section*.159}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{144}{section*.160}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{145}{section*.161}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{146}{section*.162}}
+\citation{Qui04}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{147}{section*.163}}
+\@setckpt{models/ei.dynamic}{
+\setcounter{page}{148}
+\setcounter{equation}{0}
+\setcounter{enumi}{1}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{4}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{220}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/ei.dynamic.tex b/inst/doc/models/ei.dynamic.tex
new file mode 100644
index 0000000..3fd28e7
--- /dev/null
+++ b/inst/doc/models/ei.dynamic.tex
@@ -0,0 +1,283 @@
+\section{\texttt{ei.dynamic}: Quinn's Dynamic Ecological Inference
+Model}
+
+\label{ei.dynamic}
+
+Given contingency tables with observed marginals, ecological inference
+({\sc ei}) models estimate each internal cell value for each table.
+Quinn's dynamic {\sc ei} model estimates a dynamic Bayesian model for
+$2 \times 2$ tables with temporal dependence across tables (units).
+The model is implemented using a Markov Chain Monte Carlo algorithm
+(via a combination of slice and Gibbs sampling).  
+For a hierarchical Bayesian implementation of {\sc ei} see Quinn's
+dynamic {\sc ei} model (\Sref{ei.hier}). For contingency tables larger than
+2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}). 
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL, 
+                 model = "MCMCei.dynamic", data = mydata)
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\begin{itemize}
+\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
+proportions) containing the column margins of the units to be
+analyzed.
+
+\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
+proportions) containing the row margins of the units to be
+analyzed.
+
+\item \texttt{N}: total counts in each contingency table (unit).  If
+\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
+you must specify \texttt{N}.
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional inputs for
+\texttt{ei.dynamic} to monitor the convergence of the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 5,000). 
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 50,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed of 12345. 
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to
+specify priors and other parameters:
+
+\begin{itemize}
+\item \texttt{W}: a $p \times p$ numeric matrix describing the
+structure of the temporal dependence among elements of $\theta_0$ and
+$\theta_1$. The default value is 0, which constructs a weight matrix
+corresponding to random walk priors for $\theta_0$ and $\theta_1$
+(assuming that the tables are equally spaced throughout time, and that
+the elements of \texttt{t0},
+\texttt{t1},\texttt{x0},\texttt{x1} are temporally ordered). 
+
+\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$. The default is 0.825.
+
+\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$. The default is 0.0105.
+
+\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$. The default is 0.825.
+
+\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$. The default is 0.0105.
+\end{itemize}
+
+\noindent Users may wish to refer to \texttt{help(MCMCdynamicEI)} for more options.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples \\
+ Attaching the example dataset:
+\begin{verbatim} 
+> data(eidat)
+\end{verbatim}
+Estimating the model using \texttt{ei.dynamic}:
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.dynamic", 
+                 data = eidat,  mcmc = 40000, thin = 10, burnin = 10000, 
+                 verbose = TRUE)
+> summary(z.out)
+\end{verbatim}
+
+Setting values for in-sample simulations given the marginal values 
+of {\tt t0}, {\tt t1}, {\tt x0}, and {\tt x1}:
+\begin{verbatim}
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+\end{verbatim}
+
+In-sample simulations from the posterior distribution:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+Summarizing in-sample simulations at aggregate level 
+weighted by the count in each unit:
+\begin{verbatim}
+> summary(s.out)
+\end{verbatim}
+
+Summarizing in-sample simulations at unit level for the first 5 units:
+\begin{verbatim}
+> summary(s.out, subset = 1:5)
+\end{verbatim}
+
+\end{enumerate}
+\clearpage
+\subsubsection{Model}
+Consider the following $2 \times 2$ contingency table for the racial
+voting example.  For each geographical unit $i = 1, \dots, p$, the
+marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
+would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
+\begin{table}[h!]
+  \begin{center}
+    \begin{tabular}{l|cc|c}
+      & No Vote  & Vote &         \\
+      \hline
+      Black & $n_i^{00}$  & $n_i^{01}$ & $x_i^0$   \\
+      White & $n_i^{10}$  & $n_i^{11}$ & $x_i^1$ \\
+      \hline
+      & $t_i^0$ & $t_i^1$ & $N_i$         
+    \end{tabular}
+  \end{center}
+\end{table}
+
+\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$, $t_i^1$ are
+observed as either counts or fractions. If fractions, the counts can
+be obtained by multiplying by the total counts per table $N_i =
+n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$, and rounding to the
+nearest integer.  Although there are four internal cells, only two
+unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
+$n_{i}^{11} = s_i^1 - n_{i}^{10}$. \\
+
+The hierarchical Bayesian model for ecological inference in 
+$2 \times 2$ is illustrated as following: 
+\begin{itemize}
+\item The \emph{stochastic component} of the model assumes that
+\begin{eqnarray*}
+n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
+x_i^0, \beta_i^b\right)  ,\\
+n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
+x_i^1, \beta_i^w\right)  \\
+\end{eqnarray*}
+where $\beta_{i}^{b}$ is the fraction of the black voters who vote
+and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$ 
+and $\beta_i^w$ as well as their aggregate summaries are the focus
+of inference.
+
+\item The \emph{systematic component} of the model is 
+\begin{eqnarray*}
+\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
+\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1} 
+\end{eqnarray*}
+The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$, 
+and $\theta_i^1$ now take value on the real line. (Future versions may allow
+$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
+
+\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
+ are given by
+\begin{eqnarray*}
+\theta_{i}^{0}   \mid\sigma_{0}^{2}&\propto & \frac{1}{\sigma_0^{p}} \exp
+\left( -\frac{1}{2\sigma_{0}^2}\theta_0' P \theta_0 \right) \\
+\theta_{i}^{1}   \mid\sigma_{1}^{2}&\propto & \frac{1}{\sigma_1^{p}} \exp
+\left( -\frac{1}{2\sigma_{1}^2}\theta_1' P \theta_1 \right) 
+\end{eqnarray*}
+where $P$ is a $p \times p$ matrix whose off diagonal elements
+$P_{ts}$ ($t \ne s$) equal $-W_{ts}$ (the negative values of the corresponding
+elements of the weight matrix $W$), and diagonal elements 
+$P_{tt}=\sum_{s\ne t} W_{ts}$. Scale parameters $\sigma_{0}^2$ and
+$\sigma_{1}^2$ have hyperprior distributions as given below.  
+
+\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
+given by
+\begin{eqnarray*}
+\sigma_{0}^{2}  & \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right)  ,\\
+\sigma_{1}^{2}  & \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right)  ,
+\end{eqnarray*}
+where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the 
+(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$
+ are the scale parameters. \\
+
+The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
+and $\sigma_{1}^{2}$ are chosen such that the prior distributions for
+$\beta^b$ and $\beta^w$ are flat.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL, 
+          model = "ei.dynamic", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in 
+\texttt{z.out} by using \texttt{names(z.out)}, see the draws from the
+posterior distribution of the quantities of interest by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, 
+you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the parameters.
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{N}: the total counts when the inputs are fractions.
+\item \texttt{seed}: the random seed used in the model.
+\end{itemize}
+
+\item From \texttt{summary(z.out)}, you may extract:
+\begin{itemize}
+\item \texttt{summary}: a matrix containing the summary information of the 
+posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
+the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
+the posterior distribution.  The first $p$ rows correspond to
+$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
+\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
+$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
+\texttt{p1table}$i$. The last four rows contain information about
+$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
+variances of $\theta_0$ and $\theta_1$. 
+\end{itemize}
+\item From the \texttt{sim()} output object \texttt{s.out}, you may
+extract quantities of interest arranged as arrays indexed by
+simulation $\times$ column $\times$ row $\times$ observation, where
+column and row refer to the column dimension and the row dimension of
+the ecological table, respectively. In this model, only $2 \times 2$
+contingency tables are analyzed, hence column$=2$ and row$=2$ in all
+cases. Available quantities are:
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values of each internal
+cell given the observed marginals.
+\item \texttt{qi\$pr}: the simulated expected values of each internal
+cell given the observed marginals.
+\end{itemize}
+
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The dynamic {\sc ei} model was developed in
+\begin{verse}
+\bibentry{Qui04}.
+\end{verse}
+The \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.dynamic} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/ei.hier.aux b/inst/doc/models/ei.hier.aux
new file mode 100644
index 0000000..6a4b6fb
--- /dev/null
+++ b/inst/doc/models/ei.hier.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.5}\texttt  {ei.hier}: Hierarchical Ecological Inference Model for $2 \times 2$ Tables}{148}{section.12.5}}
+\newlabel{ei.hier}{{12.5}{148}{\texttt {ei.hier}: Hierarchical Ecological Inference Model for $2 \times 2$ Tables\relax }{section.12.5}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{148}{section*.164}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{148}{section*.165}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{148}{section*.166}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{149}{section*.167}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{150}{section*.168}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{151}{section*.169}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{152}{section*.170}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{153}{section*.171}}
+\@setckpt{models/ei.hier}{
+\setcounter{page}{154}
+\setcounter{equation}{0}
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diff --git a/inst/doc/models/ei.hier.tex b/inst/doc/models/ei.hier.tex
new file mode 100644
index 0000000..787a298
--- /dev/null
+++ b/inst/doc/models/ei.hier.tex
@@ -0,0 +1,287 @@
+\section{\texttt{ei.hier}: Hierarchical Ecological Inference Model for
+$2 \times 2$ Tables} 
+
+\label{ei.hier}
+
+Given contingency tables with observed marginals, ecological inference
+({\sc ei}) models estimate each internal cell value for each table.
+The hierarchical {\sc ei} model estimates a Bayesian model for $2
+\times 2$ tables.  The model is implemented using a Markov Chain Monte
+Carlo algorithm (via a combination of slice and Gibbs sampling).  
+For a Bayesian implementation of {\sc ei} that accounts for temporal
+dependence, see Quinn's dynamic {\sc ei} model (\Sref{ei.dynamic}).
+For contingency tables larger than
+2 rows by 2 columns, see R$\times$C {\sc ei} (\Sref{eiRxC}). 
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, N = NULL, 
+                 model = "MCMCei.hier", data = mydata)
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\begin{itemize}
+\item \texttt{t0}, {\tt t1}: numeric vectors (either counts or
+proportions) containing the column margins of the units to be
+analyzed.
+
+\item \texttt{x0}, {\tt x1}: numeric vectors (either counts or
+proportions) containing the row margins of the units to be
+analyzed.
+
+\item \texttt{N}: total counts per contingency table (unit).  If
+\texttt{t0},\texttt{t1}, \texttt{x0} and \texttt{x1} are proportions,
+you must specify \texttt{N}. 
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional inputs for
+\texttt{ei.hier} to monitor the convergence of the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 5,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 50,000).  
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen.  
+
+\item \texttt{seed}: seed for the random number generator. The default is \texttt{NA} which
+corresponds to a random seed of 12345. 
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to specify 
+ prior parameters used in the model:
+
+\begin{itemize}
+\item \texttt{m0}: prior mean of $\mu_{0}$ (defaults to 0).
+
+\item \texttt{M0}: prior variance of $\mu_{0}$ (defaults to 2.287656).
+
+\item \texttt{m1}: prior mean of $\mu_{1}$ (defaults to 0).
+ 
+\item \texttt{M1}: prior variance of $\mu_{1}$ (defaults to 2.287656).
+
+\item \texttt{a0}: $a_{0}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$ (defaults to 0.825).
+
+\item \texttt{b0}: $b_{0}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{0}^{2}$ (defaults to 0.0105).
+
+\item \texttt{a1}: $a_{1}/2$ is the shape parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$ (defaults to 0.825).
+
+\item \texttt{b1}: $b_{1}/2$ is the scale parameter for the Inverse Gamma
+prior on $\sigma_{1}^{2}$ (defaults to 0.0105).
+\end{itemize}
+
+\noindent Users may wish to refer to \texttt{help(MCMChierEI)} for more information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples \\
+ Attaching the example dataset:
+ \begin{verbatim} 
+ > data(eidat)
+ \end{verbatim}
+Estimating the model using \texttt{ei.hier}:
+\begin{verbatim}
+> z.out <- zelig(cbind(t0, t1) ~ x0 + x1, model = "ei.hier", 
+                 data = eidat, mcmc = 40000, thin = 10, burnin = 10000, 
+                 verbose = TRUE)
+> summary(z.out)
+\end{verbatim}
+
+Setting values for in-sample simulations given marginal values 
+of {\tt x0}, {\tt x1}, {\tt t0}, and {\tt t1}:
+\begin{verbatim}
+> x.out <- setx(z.out, fn = NULL, cond = TRUE)
+\end{verbatim}
+
+In-sample simulations from the posterior distribution:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+Summarizing in-sample simulations at aggregate level 
+weighted by the count in each unit:
+\begin{verbatim}
+> summary(s.out)
+\end{verbatim}
+
+Summarizing in-sample simulations at unit level for the first 5 units:
+\begin{verbatim}
+> summary(s.out, subset = 1:5)
+\end{verbatim}
+
+\end{enumerate}
+\clearpage
+\subsubsection{Model}
+Consider the following $2 \times 2$ contingency table for the racial
+voting example.  For each geographical unit $i = 1, \dots, p$, the
+marginals $t_i^0$, $t_i^1$, $x_i^0$, and $x_i^1$ are known, and we
+would like to estimate $n_i^{00}$, $n_i^{01}$, $n_i^{10}$, and $n_i^{11}$.
+
+\begin{table}[!h]
+  \begin{center}
+    \begin{tabular}{l|cc|c}
+      & No Vote  & Vote &         \\
+      \hline
+      Black & $n_i^{00}$  & $n_i^{01}$ & $x_i^0$   \\
+      White & $n_i^{10}$  & $n_i^{11}$ & $x_i^1$ \\
+      \hline
+      & $t_i^0$ & $t_i^1$ & $N_i$         
+    \end{tabular}
+  \end{center}
+\end{table}
+
+\noindent The marginal values $x_{i}^0$, $x_{i}^1$, $t_i^0$,
+$t_i^1$ are observed as either counts or fractions. If fractions, the
+counts can be obtained by multiplying by the total counts per table
+$N_i = n_i^{00} + n_i^{01} + n_i^{10} + n_i^{11}$ and rounding to the
+nearest integer.  Although there are four internal cells, only two
+unknowns are modeled since $n_i^{01} = x_i^0 - n_{i}^{00}$ and
+$n_{i}^{11} = s_i^1 - n_{i}^{10}$.
+
+The hierarchical Bayesian model for ecological inference in $2 \times
+2$ is illustrated as following:
+\begin{itemize}
+\item The \emph{stochastic component} of the model assumes that
+\begin{eqnarray*}
+n_{i}^{00}\mid x_i^0,\beta_i^b &\sim& \textrm{Binomial}\left(
+x_i^0, \beta_i^b\right)  ,\\
+n_{i}^{10} \mid x_i^1, \beta_i^w &\sim& \textrm{Binomial}\left(
+x_i^1, \beta_i^w\right)  \\
+\end{eqnarray*}
+where $\beta_{i}^{b}$ is the fraction of the black voters who vote
+and $\beta_i^w$ is the fraction of the white voters who vote. $\beta_i^b$ 
+and $\beta_i^w$ as well as their aggregate level summaries are the focus
+of inference.
+
+\item The \emph{systematic component} is 
+\begin{eqnarray*}
+\beta_i^b &=& \frac{\exp\theta_i^0}{1 - \exp\theta_i^0} \\
+\beta_i^w &=& \frac{\exp\theta_i^1}{1 - \exp\theta_i^1} 
+\end{eqnarray*}
+The logit transformations of $\beta^b_i$ and $\beta^w_i$, $\theta_{i}^0$, 
+and $\theta_i^1$ now take value on the real line. (Future versions may allow
+$\beta_{i}^{b}$ and $\beta_{i}^{w}$ to be functions of observed covariates.)
+
+\item The \emph{priors} for $\theta_{i}^0$ and $\theta_{i}^1$
+ are given by
+\begin{eqnarray*}
+\theta_{i}^{0}   \mid\mu_{0},\sigma_{0}^{2}&\sim&\textrm{Normal}\left(  \mu
+_{0},\sigma_{0}^{2}\right),\\
+\theta_{i}^{1}  \mid\mu_{1},\sigma_{1}^{2}&\sim&\textrm{Normal}\left(  \mu
+_{1},\sigma_{1}^{2}\right)  
+\end{eqnarray*}
+where $\mu_{0}$ and $\mu_{1}$ are the means, and $\sigma_{0}^{2}$ 
+and $\sigma_{1}^{2}$ are the variances of the two corresponding 
+(independent) normal distributions.
+
+\item The \emph{hyperpriors} for $\mu_{0}$ and $\mu_{1}$ are given by
+\begin{eqnarray*}
+\mu_{0}    &\sim& \textrm{Normal} \left(  m_{0}, M_{0} \right)  ,\\
+\mu_{1}    &\sim& \textrm{Normal} \left(  m_{1}, M_{1} \right)  ,
+\end{eqnarray*}
+where $m_{0}$ and $m_{1}$ are the means of the (independent) normal
+distributions while $M_{0}$ and $M_{1}$ are the variances.
+
+\item The \emph{hyperpriors} for $\sigma_{0}^{2}$ and $\sigma_{1}^{2}$ are
+given by
+\begin{eqnarray*}
+\sigma_{0}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{0}}{2},\frac{b_{0}}{2}\right),\\
+\sigma_{1}^{2}& \sim& \textrm{Inverse Gamma}\left(\frac{a_{1}}{2},\frac{b_{1}}{2}\right),
+\end{eqnarray*}
+where $a_{0}/2$ and $a_{1}/2$ are the shape parameters of the
+(independent) Gamma distributions while $b_{0}/2$ and $b_{1}/2$ are
+the scale parameters. 
+
+The default hyperpriors for $\mu_{0},$ $\mu_{1}$, $\sigma_{0}^{2},$
+and $\sigma_{1}^{2}$ are chosen such that the prior distributions of
+$\beta^b$ and $\beta^w$ are flat.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+> z.out <- (cbind(t0, t1) ~ x0 + x1, N = NULL, 
+          model = "ei.hier", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in 
+\texttt{z.out} by using \texttt{names(z.out)}, 
+see the draws from the posterior distribution of
+the quantities of interest by using \texttt{z.out\$coefficients}, 
+and a default summary of information through \texttt{summary(z.out)}. 
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, 
+you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the parameters.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{N}: the total counts when the inputs are fractions.
+\item \texttt{seed}: the random seed used in the model.
+\end{itemize}
+
+\item From \texttt{summary(z.out)}, you may extract:
+\begin{itemize}
+\item \texttt{summary}: a matrix containing the summary information of the 
+posterior estimation of $\beta^b_i$ and$\beta^w_i$ for each unit and
+the parameters $\mu_0$, $\mu_1$, $\sigma_1$ and $\sigma_2$ based on
+the posterior distribution.  The first $p$ rows correspond to
+$\beta_i^b$, $i=1,\ldots p$, the row names are in the form of
+\texttt{p0table}$i$. The $(p+1)$-th to the $2p$-th rows correspond to
+$\beta_i^w$, $i=1,\ldots,p$. The row names are in the form of
+\texttt{p1table}$i$. The last four rows contain information about
+$\mu_0$, $\mu_1$, $\sigma_0^2$ and $\sigma_1^2$, the prior means and
+variances of $\theta_0$ and $\theta_1$. 
+\end{itemize}
+\item From the \texttt{sim()} output object \texttt{s.out}, you may
+extract quantities of interest arranged as arrays indexed by
+simulation $\times$ column $\times$ row $\times$ observation, where
+column and row refer to the column dimension and the row dimension of
+the contingency table, respectively. In this model, only $2 \times 2$
+contingency tables are analyzed, hence column$=2$ and row$=2$ in all
+cases. Available quantities are:
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values of each internal cell
+given the observed marginals. 
+\item \texttt{qi\$pr}: the simulated expected values of each internal cell 
+given the observed marginals.  
+\end{itemize}
+
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The hierarchical {\sc ei} \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{ei.hier} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/eiRxC.aux b/inst/doc/models/eiRxC.aux
new file mode 100644
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--- /dev/null
+++ b/inst/doc/models/eiRxC.aux
@@ -0,0 +1,42 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.6}\texttt  {ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological Inference Model for $R \times C$ Tables}{154}{section.12.6}}
+\newlabel{eiRxC}{{12.6}{154}{\texttt {ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological Inference Model for $R \times C$ Tables\relax }{section.12.6}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{154}{section*.172}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{154}{section*.173}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{154}{section*.174}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{156}{section*.175}}
+\citation{RosJiaKin01}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{157}{section*.176}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{157}{section*.177}}
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+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
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+}
diff --git a/inst/doc/models/eiRxC.tex b/inst/doc/models/eiRxC.tex
new file mode 100644
index 0000000..2155f66
--- /dev/null
+++ b/inst/doc/models/eiRxC.tex
@@ -0,0 +1,212 @@
+\section{\texttt{ei.RxC}: Hierarchical Multinomial-Dirichlet Ecological
+  Inference Model for $R \times C$ Tables}
+\label{eiRxC}
+
+Given $n$ contingency tables, each with observed marginals (column and
+row totals), ecological inference ({\sc ei}) estimates the internal
+cell values in each table.  The hierarchical Multinomial-Dirichlet
+model estimates cell counts in $R \times C$ tables. The model is
+implemented using a nonlinear least squares approximation and, with
+bootstrapping for standard errors, had good frequentist properties.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(T0, T1, T2, T3) ~ X0 + X1, 
+                 covar = NULL, 
+                 model = "eiRxC", data = mydata)
+> x.out <- setx(z.out, fn = NULL)
+> s.out <- sim(z.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+
+\begin{itemize}
+\item \texttt{T0}, \texttt{T1}, \texttt{T2},\ldots, \texttt{TC}:
+  numeric vectors (either counts, or proportions that sum to one for
+  each row) containing the column margins of the units to be analyzed.
+
+\item \texttt{X0}, {\tt X1}, {\tt X2},\ldots,{\tt XR}: numeric vectors
+  (either counts, or proportions that sum to one for each row)
+  containing the row margins of the units to be analyzed.
+
+\item {\tt covar}: (optional) a covariate that varies across tables,
+specified as \verb|covar = ~ Z1|, for example.  (The model only
+accepts one covariate.)
+\end{itemize}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+ \item Basic examples: No covariate \\
+ Attaching the example dataset:
+ \begin{verbatim} 
+ > data(Weimar)
+ \end{verbatim}
+Estimating the model:
+\begin{verbatim}
+> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~  
+                shareunemployed + shareblue + sharewhite + shareself + 
+                sharedomestic, model = "ei.RxC", data = Weimar)
+> summary(z.out)
+\end{verbatim}
+
+Setting values for in-sample simulations given marginal values:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+
+Estimate fractions of different social groups that support political parties:
+\begin{verbatim}
+> s.out <- sim(z.out)
+\end{verbatim}
+
+Summarizing fractions of different social groups that support political parties:
+\begin{verbatim}
+> summary(s.out)
+\end{verbatim}
+
+ \item Example of covariates being present in the model \\
+ Attaching the example dataset:
+ \begin{verbatim} 
+ > data(Weimar)
+ \end{verbatim}
+Estimating the model:
+\begin{verbatim}
+> z.out <- zelig(cbind(Nazi, Government, Communists, FarRight, Other) ~  
+                shareunemployed + shareblue + sharewhite + shareself + 
+                sharedomestic,
+                covar = ~ shareprotestants, 
+                model = "ei.RxC", data = Weimar)
+> summary(z.out)
+\end{verbatim}
+
+Set the covariate to its default (mean/median) value
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+
+Estimate fractions of different social groups that support political parties:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+Summarizing fractions of different social groups that support political parties:
+\begin{verbatim}
+> summary(s.out)
+\end{verbatim}
+\end{enumerate}
+
+\clearpage
+
+\subsubsection{Model}
+Consider the following $5 \times 5$ contingency table for the voting
+patterns in Weimar Germany.  For each geographical unit $i$ ($i = 1,
+\dots, p$), the marginals $T_{1i}$,\dots, $T_{Ci}$, $X_{1i}$,\dots,
+$X_{Ri}$ are known for each of the $p$ electoral precincts, and we
+would like to estimate ($\beta_i^{rc}, r=1,\dots,R, c=1,\dots,C-1$)
+which are the fractions of people in social class $r$ who vote for
+party $c$, for all $r$ and $c$.
+\begin{table}[!h]
+  \begin{center}
+    \begin{tabular}{l|ccccc|c}
+      & Nazi  & Government & Communists & Far Right & Other         \\
+      \hline
+      Unemployed & $\beta_{11}^{i}$  & $\beta_{12}^{i}$ & $\beta_{13}^{i}$  & $\beta_{14}^{i}$  & $1-\sum_{c=1}^4 \beta_{1c}^i$ & $X_1^i$   \\
+      Blue  & $\beta_{21}^{i}$  & $\beta_{22}^{i}$ & $\beta_{23}^{i}$  & $\beta_{24}^{i}$  & $1-\sum_{c=1}^4 \beta_{2c}^i$ & $X_2^i$   \\
+      White  & $\beta_{31}^{i}$  & $\beta_{32}^{i}$ & $\beta_{33}^{i}$  & $\beta_{34}^{i}$  & $1-\sum_{c=1}^4 \beta_{3c}^i$ & $X_3^i$   \\
+      Self  & $\beta_{41}^{i}$  & $\beta_{42}^{i}$ & $\beta_{43}^{i}$  & $\beta_{44}^{i}$  & $1-\sum_{c=1}^4 \beta_{4c}^i$ & $X_4^i$   \\
+      Domestic  & $\beta_{51}^{i}$  & $\beta_{52}^{i}$ & $\beta_{53}^{i}$  & $\beta_{54}^{i}$  & $1-\sum_{c=1}^4 \beta_{5c}^i$ & $X_5^i$   \\
+      \hline
+      & $T_{1i}$ & $T_{2i}$  & $T_{3i}$      & $T_{4i}$  & $1-\sum_{c=1}^4 \beta_{ci}$
+    \end{tabular}
+  \end{center}
+\end{table}
+
+\noindent 
+The marginal values $X_{1i},\dots,X_{Ri}$, $T_{1i},\dots,T_{Ci}$ may be
+observed as counts or fractions.
+
+Let $T_i^{'}=(T_{1i}^{'},T_{2i}^{'},\dots,T_{Ci}^{'})$ be the number
+of voting age persons who turn out to vote for different parties.
+There are three levels of hierarchy in the 
+Multinomial-Dirichlet {\sc ei} model.  At the first stage, we model
+the data as:
+\begin{itemize}
+
+\item The \emph{stochastic component} is described $T_i^{'}$ which
+follows a multinomial distribution: 
+\begin{eqnarray*}
+T_i^{'} &\sim & \textrm{Multinomial}(\Theta_{1i},\dots,\Theta_{Ci})
+\end{eqnarray*}
+
+\item The \emph{systematic components} are
+\begin{eqnarray*}
+\Theta_{ci}=\sum_{r=1}^{R}\beta_{rc}^{i}X_{ri} & \textrm{for} &  c=1,\dots,C
+\end{eqnarray*}
+
+\end{itemize}
+
+At the second stage, we use an optional covariate to model
+$\Theta_{ci}$'s and $\beta_{qrc}^{i}$: 
+\begin{itemize}
+\item The \emph{stochastic component} is described by
+$\beta_{r}^{i}=(\beta_{r1},\beta_{r2},\dots,\beta_{r,C-1})$ for 
+  $i=1,\dots,,p$ and $r=1,\dots,R$,  which follows a Dirichlet distribution:
+
+  \begin{eqnarray*}
+    \beta_r^{i}  &\sim & \textrm{Dirichlet}(\alpha_{r1}^{i},\dots,\alpha_{rc}^{i})
+  \end{eqnarray*}
+
+\item The \emph{systematic components} are 
+\begin{eqnarray*}
+  \alpha_{rc}^{i}=\frac{d_{r}exp(\gamma_{rc}+\delta_{rc}Z_{i})}{d_r(1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i))} = \frac{exp(\gamma_{rc}+\delta_{rc}Z_i)}{1+\sum_{j=1}^{C-1}exp(\gamma_{rj}+\delta_{rj}Z_i)} 
+\end{eqnarray*}
+for $i=1, \dots,p$, $r=1,\dots,R$, and $c=1, \dots, C-1$.
+
+In the third stage, we assume that the regression parameters (the
+$\gamma_{rc}$'s and $\delta_{rc}$'s) are \emph{a priori} independent,
+and put a flat prior on these regression parameters.  The parameters
+$d_{r}$ for $r=1,\dots,R$ are assumed to follow exponential distributions with
+mean $\frac{1}{\lambda}$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+> z.out <- zelig(cbind(T0, T1, T2) ~ X0 + X1 + X2, 
+          model = "eiRxC", data = mydata)
+\end{verbatim}
+
+\noindent then you may examine the available information in
+\texttt{z.out} by using \texttt{names(z.out)}.  For example,
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object
+  \texttt{z.out\$coefficients} are the estimates of $\gamma_{ij}$ (and
+  also $\delta_{ij}$, if covariates are present). The parameters are
+  returned as a single vector of length $R\times (C-1)$. If there is a
+  covariate, $\delta$ is concatenated to it.
+
+\item From the \texttt{sim()} output object, you may extract the
+  parameters $\beta_{ij}$ corresponding to the estimated fractions of
+  different social groups that support different political parties, by
+  using \texttt{s.out\$qi\$ev}.  For each precinct, that will be a
+  matrix with dimensions: simulations $\times R \times C$.
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Please cite the model as 
+\begin{verse}
+\bibentry{RosJiaKin01}.
+\end{verse}
+
+Jason Wittenberg, Ferdinand Alimadhi, and Olivia Lau implemented the
+$R \times C$ {\sc ei} model for Zelig.  
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/exp.aux b/inst/doc/models/exp.aux
new file mode 100644
index 0000000..9bb1954
--- /dev/null
+++ b/inst/doc/models/exp.aux
@@ -0,0 +1,46 @@
+\relax 
+\citation{Huber81}
+\citation{White80}
+\@writefile{toc}{\contentsline {section}{\numberline {12.7}{\tt  exp}: Exponential Regression for Duration Dependent Variables}{158}{section.12.7}}
+\newlabel{exp}{{12.7}{158}{{\tt exp}: Exponential Regression for Duration Dependent Variables\relax }{section.12.7}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{158}{section*.178}}
+\@writefile{toc}{\contentsline {subsubsection}{Input Values}{158}{section*.179}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{159}{section*.180}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{159}{section*.181}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{160}{section*.182}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{161}{section*.183}}
+\citation{VenRip02}
+\citation{KinAltBur90}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{162}{section*.184}}
+\@setckpt{models/exp}{
+\setcounter{page}{163}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
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+\setcounter{chapter}{12}
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+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
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+\setcounter{table}{0}
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+}
diff --git a/inst/doc/models/exp.tex b/inst/doc/models/exp.tex
new file mode 100644
index 0000000..a627925
--- /dev/null
+++ b/inst/doc/models/exp.tex
@@ -0,0 +1,275 @@
+\section{{\tt exp}: Exponential Regression for Duration
+Dependent Variables}\label{exp}
+
+Use the exponential duration regression model if you have a dependent
+variable representing a duration (time until an event).  The model
+assumes a constant hazard rate for all events.  The dependent variable
+may be censored (for observations have not yet been completed when
+data were collected).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X, model = "exp", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Exponential models require that the dependent variable be in the form
+{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+  e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study and is know to live at least as long as $y_i$).  If
+$c_i$ is omitted, all Y are assumed to be completed; that is, time
+defaults to 1 for all observations.
+
+\subsubsection{Input Values} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for exponential regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) and the options selected in
+{\tt cluster}.
+\item {\tt cluster}:  if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations.  Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata.  Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3", 
+                 model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters.  If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.  
+\end{itemize}  
+
+\subsubsection{Example}
+
+Attach the sample data: 
+\begin{verbatim}
+> data(coalition)
+\end{verbatim}
+Estimate the model: 
+\begin{verbatim}
+> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp", 
+                 data = coalition)
+\end{verbatim}
+View the regression output:  
+\begin{verbatim}
+> summary(z.out)
+\end{verbatim}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{verbatim}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{verbatim}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+\end{verbatim}
+Summarize quantities of interest and produce some plots:  
+\begin{verbatim}
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+  Y_i = \left\{ \begin{array}{ll}
+      Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+      y_c & \textrm{if }Y_i^* > y_c \\
+    \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+  of the partially observed variable $Y^*$.  We assume $Y_i^*$ follows
+  the exponential distribution whose density function is given by
+  \begin{equation*}
+    f(y_i^*\mid \lambda_i) = \frac{1}{\lambda_i} \exp\left(-\frac{y_i^*}{\lambda_i}\right)
+  \end{equation*}
+  for $y_i^*\ge 0$ and $\lambda_i>0$. The mean of this distribution is
+  $\lambda_i$.  
+
+  In addition, survival models like the exponential have three
+  additional properties.  The hazard function $h(t)$ measures the
+  probability of not surviving past time $t$ given survival up to
+  $t$. In general, the hazard function is equal to $f(t)/S(t)$ where
+  the survival function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the
+  fraction still surviving at time $t$.  The cumulative hazard
+  function $H(t)$ describes the probability of dying before time $t$.
+  In general, $H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$.  In the case
+  of the exponential model,
+\begin{eqnarray*}
+h(t) &=& \frac{1}{\lambda_i} \\
+S(t) &=& \exp\left( -\frac{t}{\lambda_i} \right) \\
+H(t) &=& \frac{t}{\lambda_i}
+\end{eqnarray*}
+For the exponential model, the hazard function $h(t)$ is constant over
+time.  The Weibull model and lognormal models allow the hazard
+function to vary as a function of elapsed time (see \Sref{weibull} and
+\Sref{lognorm} respectively).
+  
+\item The \emph{systematic component} $\lambda_i$ is modeled as
+  \begin{equation*}
+    \lambda_i = \exp(x_i \beta),
+  \end{equation*}
+  where $x_i$ is the vector of explanatory variables, and $\beta$ is
+  the vector of coefficients.
+\end{itemize}  
+
+
+\subsubsection{Quantities of Interest} 
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the exponential model are
+  simulations of the expected duration given $x_i$ and draws of
+  $\beta$ from its posterior, $$E(Y) = \lambda_i = \exp(x_i \beta).$$
+
+\item The predicted values ({\tt qi\$pr}) are draws from the
+  exponential distribution with rate equal to the expected value.  
+
+\item The first difference (or difference in expected values, {\tt
+  qi\$ev.diff}), is
+\begin{equation}
+\textrm{FD} \; = \; E(Y \mid x_1) - E(Y \mid x), 
+\end{equation}
+where $x$ and $x_1$ are different vectors of values for the
+explanatory variables.  
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is \begin{equation*}
+  \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) - E[Y_i(t_i=0)]
+  \right\}, \end{equation*} where $t_i$ is a binary explanatory
+  variable defining the treatment ($t_i=1$) and control ($t_i=0$)
+  groups. When $Y_i(t_i=1)$ is censored rather than observed, we
+  replace it with a simulation from the model given available
+  knowledge of the censoring process.  Variation in the simulations
+  is due to two factors: uncertainty in the imputation process for
+  censored $y_i^*$ and uncertainty in simulating $E[Y_i(t_i=0)]$, the
+  counterfactual expected value of $Y_i$ for observations in the
+  treatment group, under the assumption that everything stays the same
+  except that the treatment indicator is switched to $t_i=0$.
+    
+  \item In conditional prediction models, the average predicted
+  treatment effect ({\tt att.pr}) for the treatment group is
+  \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+  \widehat{Y_i(t_i=0)} \right\}, \end{equation*} where $t_i$ is a
+  binary explanatory variable defining the treatment ($t_i=1$) and
+  control ($t_i=0$) groups.  When $Y_i(t_i=1)$ is censored rather than
+  observed, we replace it with a simulation from the model given
+  available knowledge of the censoring process.  Variation in the
+  simulations is due to two factors: uncertainty in the imputation
+  process for censored $y_i^*$ and uncertainty in simulating
+  $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
+  for observations in the treatment group, under the assumption that
+  everything stays the same except that the treatment indicator is
+  switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(Surv(Y,
+  C) \~\, X, model = "exp", data)}, then you may examine the
+available information in \texttt{z.out} by using
+\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
+  z.out\$coefficients}, and a default summary of information
+through \texttt{summary(z.out)}.  Other elements available through
+the {\tt \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt icoef}: parameter estimates for the intercept and scale
+     parameter.  While the scale parameter varies for the Weibull
+     distribution, it is fixed to 1 for the exponential distribution
+     (which is modeled as a special case of the Weibull).  
+   \item {\tt var}: the variance-covariance matrix for the estimates
+     of $\beta$.  
+   \item {\tt loglik}: a vector containing the log-likelihood for the
+     model and intercept only (respectively).
+   \item {\tt linear.predictors}: the vector of
+     $x_{i}\beta$.
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model. 
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item Most of this may be conveniently summarized using {\tt
+   summary(z.out)}.  From {\tt summary(z.out)}, you may
+ additionally extract: 
+   \begin{itemize}
+   \item {\tt table}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.  For
+     example, {\tt summary(z.out)\$table}
+   \end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt s.out}:
+  
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values for the specified
+     values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from a
+     distribution defined by the expected values.
+   \item {\tt qi\$fd}: the simulated first differences between the
+     simulated expected values for {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The exponential function is part of the survival library by Terry
+Therneau, ported to R by Thomas Lumley.  Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality. 
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/factor.bayes.aux b/inst/doc/models/factor.bayes.aux
new file mode 100644
index 0000000..954fbbd
--- /dev/null
+++ b/inst/doc/models/factor.bayes.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.8}\texttt  {factor.bayes}: Bayesian Factor Analysis}{163}{section.12.8}}
+\newlabel{factor.bayes}{{12.8}{163}{\texttt {factor.bayes}: Bayesian Factor Analysis\relax }{section.12.8}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{163}{section*.185}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{163}{section*.186}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{163}{section*.187}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{165}{section*.188}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{165}{section*.189}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{166}{section*.190}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{167}{section*.191}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{167}{section*.192}}
+\@setckpt{models/factor.bayes}{
+\setcounter{page}{168}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{8}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{225}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/factor.bayes.tex b/inst/doc/models/factor.bayes.tex
new file mode 100644
index 0000000..c8ae634
--- /dev/null
+++ b/inst/doc/models/factor.bayes.tex
@@ -0,0 +1,260 @@
+\section{\texttt{factor.bayes}: Bayesian Factor Analysis}\label{factor.bayes}
+
+Given some unobserved explanatory variables and observed dependent
+variables, the Normal theory factor analysis model estimates the
+latent factors.  The model is implemented using a Markov Chain Monte
+Carlo algorithm (Gibbs sampling with data augmentation).  For factor
+analysis with ordinal dependent variables, see ordered factor analysis
+(\Sref{factor.ord}), and for a mix of types of dependent variables,
+see the mixed factor analysis model (\Sref{factor.mix}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 2, 
+                model = "factor.bayes", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} takes the following functions for {\tt factor.bayes}:  
+\begin{itemize}
+\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: variables of interest in 
+factor analysis (manifest variables), assumed to be normally
+distributed. The model requires a minimum of three manifest variables.
+
+\item \texttt{factors}: number of the factors to be fitted (defaults to 2).
+
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following additional arguments for 
+model specification: 
+\begin{itemize}
+\item \texttt{lambda.constraints}: list containing the equality or 
+inequality constraints on the factor loadings.  Choose from one of the
+following forms:
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize} 
+
+\item \texttt{std.var}: defaults to {\tt FALSE} (manifest variables
+are rescaled to zero mean, but retain observed variance).  If
+\texttt{TRUE}, the manifest variables are rescaled to be mean zero and
+unit variance.
+
+\end{itemize}
+ In addition, {\tt zelig()} accepts the following additional inputs
+for {\tt bayes.factor}:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000). 
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345. 
+
+\item \texttt{Lambda.start}: starting values of the factor loading
+matrix $\Lambda$, either a scalar (all unconstrained loadings are set
+to that value), or a matrix with compatible dimensions.  The default
+is \texttt{NA}, where the start value are set to be 0 for
+unconstrained factor loadings, and 0.5 or $-$0.5 for constrained
+factor loadings (depending on the nature of the constraints).
+
+\item \texttt{Psi.start}: starting values for the uniquenesses, either
+a scalar (the starting values for all diagonal elements of $\Psi$ are
+set to be this value), or a vector with length equal to the number of
+manifest variables.  In the latter case, the starting values of the
+diagonal elements of $\Psi$ take the values of \texttt{Psi.start}. The
+default value is \texttt{NA} where the starting values of the all the
+uniquenesses are set to be 0.5.
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
+posterior draws of the factor loadings.  
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}.  If {\tt TRUE},
+stores the posterior draws of the factor scores.  (Storing factor
+scores may take large amount of memory for a large number of draws
+or observations.)
+
+\end{itemize}
+
+\noindent The model also accepts the following additional arguments to 
+specify prior parameters:
+
+\begin{itemize}
+
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$.  If a scalar value, that value will be the prior mean for
+all the factor loadings. Defaults to 0.
+
+\item \texttt{L0}: precision parameter of the Normal prior 
+for the factor loadings, either a scalar or a matrix with the same
+dimensions as $\Lambda$.  If \texttt{L0} takes a scalar value, then
+the precision matrix will be a diagonal matrix with the diagonal
+elements set to that value. The default value is 0, which leads to an
+improper prior.
+
+\item \texttt{a0}: the shape parameter of the Inverse Gamma prior for 
+the uniquenesses is \texttt{a0/2}. It can take a scalar value or a
+vector. The default value is 0.001.
+
+\item \texttt{b0}: the shape parameter of the Inverse Gamma prior for 
+the uniquenesses is \texttt{b0/2}. It can take a scalar value or a
+vector. The default value is 0.001.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCfactanal)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(swiss)
+> names(swiss) <- c("Fert","Agr","Exam","Educ","Cath","InfMort")
+\end{verbatim}
+Factor analysis:
+\begin{verbatim}
+> z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL, 
+                 model = "factor.bayes", data = swiss, factors = 2,
+	         verbose = TRUE, a0 = 1, b0 = 0.15, 
+                 burnin = 5000, mcmc = 50000)
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+\end{verbatim} %$
+
+Since the algorithm did not converge, we now add some constraints on
+$\Lambda$. 
+
+\item {Putting Constraints on $\Lambda$} \\
+Put constraints on Lambda to optimize the algorithm:
+\begin{verbatim} 
+z.out <- zelig(cbind(Agr, Exam, Educ, Cath, InfMort) ~ NULL,  
+               model = "factor.bayes", data = swiss, factors = 2,
+               lambda.constraints = list(Exam=list(1,"+"),
+                                    Exam=list(2,"-"), Educ=c(2,0),
+                                    InfMort=c(1,0)), 
+               verbose = TRUE, a0 = 1, b0 = 0.15, 
+               burnin = 5000, mcmc = 50000)
+
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out) 
+\end{verbatim} %$
+\end{enumerate}
+
+\subsubsection{Model} 
+Suppose for observation $i$ we observe $K$ variables and hypothesize
+that there are $d$ underlying factors such that:  
+\begin{eqnarray*}
+Y_i = \Lambda \phi_i+\epsilon_i
+\end{eqnarray*}
+where $Y_{i}$ is the vector of $K$ manifest variables for observation
+$i$. $\Lambda$ is the $K \times d$ factor loading matrix and $\phi_i$
+is the $d$-vector of latent factor scores. Both $\Lambda$ and $\phi$
+need to be estimated.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by:
+\begin{eqnarray*}
+\epsilon_{i}  \sim \textrm{Normal}(0, \Psi).
+\end{eqnarray*}
+where $\Psi$ is a diagonal, positive definite matrix. The diagonal elements
+of $\Psi$ are referred to as uniquenesses.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_i = E(Y_i) = \Lambda\phi_i
+\end{eqnarray*}
+
+\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$  is given by
+\begin{eqnarray*}
+\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d. 
+\end{eqnarray*}
+
+\item The independent conjugate \emph{prior} for each $\Psi_{ii}$ is given by
+\begin{eqnarray*}
+\Psi_{ii} \sim \textrm{InverseGamma}(\frac{a_0}{2}, \frac{b_0}{2}), \textrm{ for } 
+i = 1, \ldots, k.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is
+\begin{eqnarray*}
+\phi_i &\sim& \textrm{Normal}(0, I_d), \textrm{ for } i = 1, \ldots, n.
+\end{eqnarray*}
+where $I_d$ is a $ d\times d $ identity matrix.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings and the uniquenesses.  If
+\texttt{store.scores = TRUE}, the estimated factors scores are also
+contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.   
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian factor analysis \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/factor.mix.aux b/inst/doc/models/factor.mix.aux
new file mode 100644
index 0000000..5b6ab7b
--- /dev/null
+++ b/inst/doc/models/factor.mix.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.9}\texttt  {factor.mix}: Mixed Data Factor Analysis}{168}{section.12.9}}
+\newlabel{factor.mix}{{12.9}{168}{\texttt {factor.mix}: Mixed Data Factor Analysis\relax }{section.12.9}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{168}{section*.193}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{168}{section*.194}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{168}{section*.195}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{170}{section*.196}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{171}{section*.197}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{171}{section*.198}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{172}{section*.199}}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{173}{section*.200}}
+\@setckpt{models/factor.mix}{
+\setcounter{page}{174}
+\setcounter{equation}{1}
+\setcounter{enumi}{1}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{9}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
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+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{226}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
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+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/factor.mix.tex b/inst/doc/models/factor.mix.tex
new file mode 100644
index 0000000..ea04815
--- /dev/null
+++ b/inst/doc/models/factor.mix.tex
@@ -0,0 +1,272 @@
+\section{\texttt{factor.mix}: Mixed Data Factor Analysis}\label{factor.mix}
+
+Mixed data factor analysis takes both continuous and ordinal dependent
+variables and estimates a model for a given number of latent factors.
+The model is estimated using a Markov Chain Monte Carlo algorithm
+(Gibbs sampler with data augmentation).  Alternative models include
+Bayesian factor analysis for continuous variables
+(\Sref{factor.bayes}) and Bayesian factor analysis for ordinal
+variables (\Sref{factor.ord}).  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1, 
+                model = "factor.mix", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments for {\tt factor.mix}:  
+\begin{itemize}
+
+\item \texttt{Y1}, {\tt Y2}, \texttt{Y3}, {\tt \dots}: The dependent variables of
+interest, which can be a mix of ordinal and continuous variables.  You
+must have more dependent variables than factors.
+
+\item \texttt{factors}: The number of the factors to be fitted.
+
+\end{itemize}
+
+
+\subsubsection{Additional Inputs}
+
+The model accepts the following additional arguments to monitor
+convergence:  
+\begin{itemize}
+\item \texttt{lambda.constraints}: A list that contains the equality or 
+inequality constraints on the factor loadings. 
+
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative.
+\end{itemize} 
+Unlike Bayesian factor analysis for continuous variables
+(\Sref{factor.bayes}), the first column of $\Lambda$ corresponds to
+negative item difficulty parameters and should not be constrained in
+general.
+
+\item \texttt{std.mean}: defaults to {\tt TRUE}, which rescales the
+continuous manifest variables to have mean 0.  
+
+\item \texttt{std.var}: defaults to {\tt TRUE}. which rescales the
+continuous manifest variables to have unit variance.  
+\end{itemize}
+
+
+\noindent \texttt{factor.mix} accepts the following additional arguments 
+to monitor the sampling scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded. The default value is 1,000.
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin.
+ The default value is 20,000.
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: tuning parameter, which can be either a
+scalar or a vector of length $K$. The value of the tuning parameter
+must be positive. The default value is \texttt{1.2}.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen. The default 
+is \texttt{FALSE}.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{lambda.start}: starting values of the factor loading
+matrix $\Lambda$ for the Markov chain, either a scalar (starting
+values of the unconstrained loadings will be set to that value), or a
+matrix with compatible dimensions.  The default is \texttt{NA}, where
+the start values for the first column of $\Lambda$ are set based on
+the observed pattern, while for the rest of the columns of $\Lambda$,
+the start values are set to be 0 for unconstrained factor loadings,
+and 1 or $-$1 for constrained factor loadings (depending on the nature
+of the constraints).
+
+\item \texttt{psi.start}: starting values for the diagonals of the error variance
+(uniquenesses) matrix. Since the starting values for the ordinal
+variables are constrained to 1 (to identify the model), you may only
+specify the starting values for the continuous variables.  For the
+continuous variables, you may specify {\tt psi.start} as a scalar or a
+vector with length equal to the number of continuous variables.  If a
+scalar, that starting value is recycled for all continuous variables.
+If a vector, the starting values should correspond to each of the
+continuous variables.  The default value is \texttt{NA}, which means
+the starting values of all the continuous variable uniqueness are set
+to 0.5.
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, storing the
+posterior draws of the factor loadings.  
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}.  If {\tt TRUE},
+the posterior draws of the factor scores are stored. (Storing factor
+scores may take large amount of memory for a a large number of draws
+or observations.)
+
+\end{itemize}
+Use the following additional arguments to specify prior parameters used in the model:
+\begin{itemize}
+
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$.  If a scalar value, then that value will be the prior mean
+for all the factor loadings. The default value is 0.
+
+\item \texttt{L0}: precision parameter of Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as $\Lambda$.  
+If a scalar value, then the precision matrix will be 
+a diagonal matrix with the diagonal elements set to that value. 
+The default value is 0 which leads to an improper prior.
+
+\item \texttt{a0}: {\tt a0/2} is the shape parameter of the Inverse Gamma priors for 
+the uniquenesses. It can take a scalar value or a vector. The default
+value is 0.001.
+
+\item \texttt{b0}: {\tt b0/2} is the shape parameter of the Inverse Gamma priors for 
+the uniquenesses. It can take a scalar value or a vector. The default
+value is 0.001.
+
+\end{itemize}
+Zelig users may wish to refer to \texttt{help(MCMCmixfactanal)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(PErisk)
+\end{verbatim}
+Factor analysis for mixed data using \texttt{factor.mix}:
+\begin{verbatim}
+> z.out<-zelig(cbind(courts, barb2, prsexp2, prscorr2, gdpw2) ~ NULL, 
+               data = PErisk, model = "factor.mix", factors = 1,  
+               burnin = 5000, mcmc = 100000, thin = 50, verbose = TRUE, 
+               L0 = 0.25)
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim}
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a $K$-vector of observed variables for observation $i$, 
+The $k$th variable can be either continuous or ordinal. When $Y_{ik}$ is an
+ordinal variable, it takes value from 1 to $J_k$ for $k=1,\ldots, K$ and for 
+$i =1, \ldots, n$. The distribution of $Y_{ik}$ is assumed to be 
+governed by another $K$-vector of unobserved continuous variable $Y_{ik}^*$. 
+There are $d$ underlying factors. When $Y_{ik}$ is continuous, we let 
+$Y_{ik}^*=Y_{ik}$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described in terms of $Y_i^*$:
+\begin{eqnarray*}
+Y_{i}^*  &\sim& \textrm{Normal}_K (\mu_i, I_K),
+\end{eqnarray*}
+where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
+
+For ordinal $Y_{ik}$,
+\begin{eqnarray*}
+Y_{ik} = j \quad {\rm if} \quad \gamma_{(j-1),k} \le Y_{ik}^* \le
+\gamma_{jk} \quad \textrm{ for } \quad j=1,\ldots, J_k; k=1,\ldots, K.
+\end{eqnarray*}
+where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for the $k$th
+variable with the following constraints, $\gamma_{lk} < \gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k k}=\infty$ for any $k=1, \ldots, K$.
+It follows that the probability of observing $Y_{ik}$ belonging to category 
+$j$ is,
+\begin{eqnarray*}
+\Pr(Y_{ik}=j) &=&\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \quad 
+\textrm{ for } j=1,\ldots,J_k
+\end{eqnarray*}
+where $\Phi(\cdot\mid\mu_{ik})$ is the cumulative distribution function of the 
+Normal distribution with mean $\mu_{ik}$ and variance 1.
+
+\item The \emph{systematic component} is given by,
+\begin{eqnarray*}
+\mu_i &=& \Lambda\phi_i,
+\end{eqnarray*}
+where $\Lambda$ is a $K \times d$ matrix of factor loadings for each variable,
+$\phi_i$ is a $d$-vector of factor scores for observation $i$. Note both 
+$\Lambda$ and $\phi$ are estimated.. 
+
+\item The independent conjugate \emph{prior} for each $\Lambda_{ij}$  is given by
+\begin{eqnarray*}
+\Lambda_{ij} &\sim& \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1}) 
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is,
+\begin{eqnarray*}
+\phi_{i} \sim \textrm{Normal}(0, I_{d-1}), \quad {\rm for} \quad i=2, \ldots, n.
+\end{eqnarray*}
+where $I_{d-1}$ is a $ (d-1) \times (d-1) $ identity matrix. Note the
+first element of $\phi_i$ is 1. 
+\end{itemize}
+
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.mix", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings, the estimated cut points $\gamma$ for each
+variable. Note the first element of $\gamma$ is normalized to be 0. If 
+\texttt{store.scores = TRUE}, the estimated factors scores are also contained in 
+\texttt{coefficients}.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.   
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()} procedure is
+not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+Factor analysis for mixed dependent variables \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.mix} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/models/factor.ord.aux b/inst/doc/models/factor.ord.aux
new file mode 100644
index 0000000..09fdaf4
--- /dev/null
+++ b/inst/doc/models/factor.ord.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.10}\texttt  {factor.ord}: Ordinal Data Factor Analysis}{174}{section.12.10}}
+\newlabel{factor.ord}{{12.10}{174}{\texttt {factor.ord}: Ordinal Data Factor Analysis\relax }{section.12.10}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{174}{section*.201}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{174}{section*.202}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{174}{section*.203}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{176}{section*.204}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{176}{section*.205}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{177}{section*.206}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{178}{section*.207}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{178}{section*.208}}
+\@setckpt{models/factor.ord}{
+\setcounter{page}{179}
+\setcounter{equation}{1}
+\setcounter{enumi}{1}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
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+\setcounter{chapter}{12}
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+\setcounter{subparagraph}{0}
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+}
diff --git a/inst/doc/models/factor.ord.tex b/inst/doc/models/factor.ord.tex
new file mode 100644
index 0000000..11437b2
--- /dev/null
+++ b/inst/doc/models/factor.ord.tex
@@ -0,0 +1,244 @@
+\section{\texttt{factor.ord}: Ordinal Data Factor Analysis}\label{factor.ord}
+
+Given some unobserved explanatory variables and observed ordinal
+dependent variables, this model estimates latent factors using a Gibbs
+sampler with data augmentation.  For factor analysis for continuous
+data, see \Sref{factor.bayes}.  For factor analysis for mixed data
+(including both continuous and ordinal variables), see
+\Sref{factor.mix}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1 ,Y2, Y3) ~ NULL, factors = 1, 
+                model = "factor.ord", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments for {\tt factor.ord}:  :
+\begin{itemize}
+\item \texttt{Y1, Y2}, and \texttt{Y3}: variables of interest in
+factor analysis (manifest variables), assumed to be ordinal
+variables. The number of manifest variables must be greater than the
+number of the factors.
+
+\item \texttt{factors}: number of the factors to be fitted (defaults
+to 1).
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+In addition, {\tt zelig()} accepts the following arguments for model
+specification:  
+\begin{itemize}
+\item \texttt{lambda.constraints}: list that contains the equality or 
+inequality constraints on the factor loadings. A typical entry in the list
+has one of the following forms: 
+\begin{itemize}
+\item {\tt varname = list()}: by default, no constraints are imposed.
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th loading for the variable named \texttt{varname} to be equal to \texttt{c};
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be positive;
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th loading for the variable named \texttt{varname} to be negative. 
+\end{itemize}
+%Unlike \texttt{factanal} the $\Lambda$ matrix 
+%has \texttt{factors+1} columns. 
+The first column of $\Lambda$ should not be constrained in general.
+
+\item \texttt{drop.constantvars}: defaults to {\tt TRUE}, dropping the
+manifest variables that have no variation before fitting the model. 
+
+\end{itemize}
+
+The model accepts the following arguments to monitor the convergence
+of the Markov chain:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: tuning parameter for Metropolis-Hasting sampling,
+either a scalar or a vector of length $K$. The value of the tuning
+parameter must be positive. The default value is 1.2.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
+progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The 
+default is \texttt{NA} which corresponds to a random seed 12345. 
+
+\item \texttt{Lambda.start}: starting values of the factor loading
+matrix $\Lambda$ for the Markov chain, either a scalar (all
+unconstrained loadings are set to that value), or a matrix with
+compatible dimensions.  The default is {\tt NA}, such that the start
+values for the first column are set based on the observed pattern,
+while the remaining columns have start values set to 0 for
+unconstrained factor loadings, and -1 or 1 for constrained loadings
+(depending on the nature of the constraints).  
+
+\item \texttt{store.lambda}: defaults to {\tt TRUE}, which stores the
+posterior draws of the factor loadings.  
+
+\item \texttt{store.scores}: defaults to {\tt FALSE}.  If {\tt TRUE},
+stores the posterior draws of the factor scores.  (Storing factor
+scores may take large amount of memory for a a large number of draws
+or observations.)
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:  
+\begin{itemize}
+\item \texttt{l0}: mean of the Normal prior for the factor
+loadings, either a scalar or a matrix with the same dimensions as
+$\Lambda$.  If a scalar value, that value will be the prior mean for
+all the factor loadings. Defaults to 0.
+
+\item \texttt{L0}: precision parameter of the Normal prior 
+for the factor loadings, either a scalar or a matrix with the same
+dimensions as $\Lambda$.  If \texttt{L0} takes a scalar value, then
+the precision matrix will be a diagonal matrix with the diagonal
+elements set to that value. The default value is 0, which leads to an
+improper prior.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCordfactanal)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(newpainters)
+\end{verbatim}
+Factor analysis for ordinal data using \texttt{factor.ord}:
+\begin{verbatim}
+> z.out <- zelig(cbind(Composition,Drawing,Colour,Expression)~NULL,
+                    data=newpainters, model="factor.ord",
+                    factors=1, L0=0.5,
+                    burin=1000,mcmc=10000, thin=5, verbose=TRUE)
+                    
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out) 
+\end{verbatim} %$
+
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of $K$ observed ordinal variables for
+observation $i$, each ordinal variable $k$ for $k=1,\ldots, K$ takes
+integer value $j=1, \ldots, J_k$. The distribution of $Y_i$ is assumed
+to be governed by another $k$-vector of unobserved continuous variable
+$Y_i^*$. There are $d$ underlying factors.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described in terms of the latent
+variable $Y_i^*$:
+\begin{eqnarray*}
+Y_{i}^*  \sim \textrm{Normal}_K(\mu_i, I_K),
+\end{eqnarray*}
+where $Y_i^*=(Y_{i1}^*, \ldots, Y_{iK}^*)$, and $\mu_i$ is the mean vector
+for $Y_i^*$, and $\mu_i=(\mu_{i1},\ldots, \mu_{iK})$.
+
+Instead of $Y_{ik}^*$, we observe ordinal  variable $Y_{ik}$,
+\begin{eqnarray*}
+Y_{ik} = j \textrm{ if } \gamma_{(j-1),k} \le Y_{ik}^* \le \gamma_{jk}
+\textrm{ for } \quad j=1,\ldots, J_k, k=1,\ldots, K.
+\end{eqnarray*}
+where $\gamma_{jk}, j=0,\ldots, J$ are the threshold parameters for
+the $k$th variable with the following constraints, $\gamma_{lk} <
+\gamma_{mk}$ for $l < m$, and $\gamma_{0k}=-\infty, \gamma_{J_k
+k}=\infty$ for any $k=1, \ldots, K$.  It follows that the probability
+of observing $Y_{ik}$ belonging to category $j$ is,
+\begin{eqnarray*}
+\Pr(Y_{ik}=j) =\Phi(\gamma_{jk} \mid \mu_{ik})-\Phi(\gamma_{(j-1),k} \mid \mu_{ik}) \textrm{ for } j=1,\ldots,J_k
+\end{eqnarray*}
+where $\Phi(\cdot \mid\mu_{ik})$ is the cumulative distribution
+function of the Normal distribution with mean $\mu_{ik}$ and variance
+1.
+
+\item The \emph{systematic component} is given by,
+\begin{eqnarray*}
+\mu_i = \Lambda\phi_i,
+\end{eqnarray*}
+where $\Lambda$ is a $K \times d$ matrix of factor loadings for each
+variable, $\phi_i$ is a $d$-vector of factor scores for observation
+$i$. Note both $\Lambda$ and $\phi$ need to be estimated.
+
+\item The independent conjugate \emph{prior} for each element of $\Lambda$,
+$\Lambda_{ij}$ is given by
+\begin{eqnarray*}
+\Lambda_{ij} \sim \textrm{Normal}(l_{0_{ij}}, L_{0_{ij}}^{-1})
+\textrm{ for } i=1,\ldots, k; \quad j=1,\ldots, d.
+\end{eqnarray*}
+
+\item The \emph{prior} for $\phi_i$ is,
+\begin{eqnarray*}
+\phi_{i(2:d)} \sim \textrm{Normal}(0, I_{d-1}), \textrm{ for } \quad i=2, \ldots, n.
+\end{eqnarray*}
+where $I_{d-1}$ is a $ (d-1)\times (d-1) $ identity matrix. Note the
+first element of $\phi_i$ is 1.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3), model = "factor.ord", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior
+distribution of the \texttt{coefficients} by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated factor loadings, the estimated cut points $\gamma$ for each
+variable. Note the first element of $\gamma$ is normalized to be 0. If 
+\texttt{store.scores=TRUE}, the estimated factors scores are also contained in 
+\texttt{coefficients}.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+
+\item \texttt{seed}: the random seed used in the model.   
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()} procedure is
+not applicable for factor analysis models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The \texttt{factor.ord} \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{factor.ord} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/gamma.aux b/inst/doc/models/gamma.aux
new file mode 100644
index 0000000..3c21ea3
--- /dev/null
+++ b/inst/doc/models/gamma.aux
@@ -0,0 +1,50 @@
+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.11}{\tt  gamma}: Gamma Regression for Continuous, Positive Dependent Variables}{179}{section.12.11}}
+\newlabel{gamma}{{12.11}{179}{{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables\relax }{section.12.11}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{179}{section*.209}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{179}{section*.210}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{179}{section*.211}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{180}{section*.212}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{180}{section*.213}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{181}{section*.214}}
+\citation{VenRip02}
+\citation{McCNel89}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{182}{section*.215}}
+\citation{KinAltBur90}
+\@setckpt{models/gamma}{
+\setcounter{page}{184}
+\setcounter{equation}{1}
+\setcounter{enumi}{1}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{11}
+\setcounter{subsection}{0}
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diff --git a/inst/doc/models/gamma.tex b/inst/doc/models/gamma.tex
new file mode 100644
index 0000000..0bf6773
--- /dev/null
+++ b/inst/doc/models/gamma.tex
@@ -0,0 +1,235 @@
+\section{{\tt gamma}: Gamma Regression for Continuous, Positive Dependent Variables}\label{gamma}
+
+Use the gamma regression model if you have a positive-valued dependent
+variable such as the number of years a parliamentary cabinet endures,
+or the seconds you can stay airborne while jumping.  The gamma
+distribution assumes that all waiting times are complete by the end
+of the study (censoring is not allowed).
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "gamma", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for gamma regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}).  The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame).  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Attach the sample data: 
+\begin{verbatim}
+> data(coalition)
+\end{verbatim}
+Estimate the model: 
+\begin{verbatim}
+> z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = coalition)
+\end{verbatim}
+View the regression output:  
+\begin{verbatim}
+> summary(z.out)
+\end{verbatim}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{verbatim}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{verbatim}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The Gamma distribution with scale parameter $\alpha$ has a
+\emph{stochastic component}:
+\begin{eqnarray*}
+Y &\sim& \textrm{Gamma}(y_i \mid \lambda_i, \alpha) \\
+f(y)  &=& \frac{1}{\alpha^{\lambda_i} \, \Gamma \lambda_i} \, y_i^{\lambda_i
+  - 1} \exp -\left\{ \frac{y_i}{\alpha} \right\}
+\end{eqnarray*}
+for $\alpha, \lambda_i, y_i > 0$.  \\
+
+\item The \emph{systematic component} is given by
+\begin{equation*}
+  \lambda_i = \frac{1}{x_i \beta}
+\end{equation*}
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean
+  of the stochastic component given draws of $\alpha$ and
+  $\beta$ from their posteriors:  $$E(Y) = \alpha_i \lambda.$$  
+\item The predicted values ({\tt qi\$pr}) are draws from the gamma
+  distribution for each given set of parameters $(\alpha, \lambda_i)$.
+\item If {\tt x1} is specified, {\tt sim()} also returns the
+  differences in the expected values ({\tt qi\$fd}), $$E(Y \mid x_1) -
+  E(Y \mid x)$$.
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.  
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "gamma", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: the vector of fitted values.
+   \item {\tt linear.predictors}: the vector of $x_{i}\beta$.
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values for the specified
+     values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from a
+     distribution defined by $(\alpha_i, \lambda)$.
+   \item {\tt qi\$fd}: the simulated first difference in the expected
+     values for the specified values in {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The gamma model is part of the stats package by William N. Venables and
+Brian D. Ripley.  Users should cite this model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse} 
+
+In addition, advanced users may wish to refer to
+\texttt{help(glm)} and \texttt{help(family)}, as well as
+\begin{verse}
+\bibentry{McCNel89}.
+\end{verse}
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinAltBur90}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/irt1d.aux b/inst/doc/models/irt1d.aux
new file mode 100644
index 0000000..991612c
--- /dev/null
+++ b/inst/doc/models/irt1d.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.12}\texttt  {irt1d}: One Dimensional Item Response Model}{184}{section.12.12}}
+\newlabel{irt1d}{{12.12}{184}{\texttt {irt1d}: One Dimensional Item Response Model\relax }{section.12.12}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{184}{section*.216}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{184}{section*.217}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional arguments}{184}{section*.218}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{186}{section*.219}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{186}{section*.220}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{187}{section*.221}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{188}{section*.222}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{188}{section*.223}}
+\@setckpt{models/irt1d}{
+\setcounter{page}{189}
+\setcounter{equation}{1}
+\setcounter{enumi}{1}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{12}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{228}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/irt1d.tex b/inst/doc/models/irt1d.tex
new file mode 100644
index 0000000..2758a39
--- /dev/null
+++ b/inst/doc/models/irt1d.tex
@@ -0,0 +1,250 @@
+\section{\texttt{irt1d}: One Dimensional Item Response Model}
+
+\label{irt1d}
+
+Given several observed dependent variables and an unobserved
+explanatory variable, item response theory estimates the latent
+variable (ideal points).  The model is estimated using the Markov
+Chain Monte Carlo algorithm via a Gibbs sampler and data augmentation.
+Use this model if you believe that the ideal points lie in one
+dimension, and see the $k$-dimensional item response model
+(\Sref{irtkd}) for $k$ hypothesized latent variables.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\texttt{irt1d} accepts the following argument:
+\begin{itemize}
+\item \texttt{Y1, Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for 
+subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and
+so on.
+\end{itemize}
+
+\subsubsection{Additional arguments}
+
+\texttt{irt1d} accepts the following additional arguments for model specification:
+
+\begin{itemize}
+\item \texttt{theta.constraints}: a list specifying possible equality
+or inequality constraints on the ability parameters $\theta$.  A
+typical entry takes one of the following forms:  
+
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = c}:  constrains the ability parameter for the subject named 
+\texttt{varname} to be equal to \texttt{c}. 
+
+\item \texttt{varname = "+"}: constrains the ability parameter for the subject named 
+\texttt{varname} to be positive. 
+
+\item \texttt{varname = "-"}: constrains the ability parameter for the
+subject named {\tt varname} to be negative.
+
+\end{itemize}
+\end{itemize} 
+
+The model also accepts the following arguments to monitor the sampling
+scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen. 
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{theta.start}: starting values for the subject abilities
+(ideal points), either a scalar or a vector with length equal to the
+number of subjects. If a scalar, that value will be the starting value
+for all subjects. The default is \texttt{NA}, which sets the starting
+values based on an eigenvalue-eigenvector decomposition of the
+agreement score matrix formed from the model response matrix
+(\texttt{cbind(Y1, Y2, ...)}).
+
+\item \texttt{alpha.start}: starting values for the difficulty
+parameters $\alpha$, either a scalar or a vector with length equal to
+the number of the items. If a scalar, the value will be the starting
+value for all $\alpha$. The default is \texttt{NA}, which sets the
+starting values based on a series of probit regressions that condition
+on \texttt{theta.start}.
+
+\item \texttt{beta.start}: starting values for the $\beta$ discrimination
+parameters, either a scalar or a vector with length equal to the
+number of the items. If a scalar, the value will be the starting value
+for all $\beta$. The default is \texttt{NA}, which sets the starting
+values based on a series of probit regressions conditioning on
+\texttt{theta.start}.
+
+\item \texttt{store.item}: defaults to {\tt TRUE}, storing the
+posterior draws of the item parameters.  (For a large number of draws or
+a large number observations, this may take a lot of memory.)  
+
+\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
+items with no variation before fitting the model.  
+
+\end{itemize}
+
+\noindent \texttt{irt1d} accepts the following additional arguments to 
+specify prior parameters used in the model:
+
+\begin{itemize}
+
+\item \texttt{t0}: prior mean of the subject abilities
+(ideal points). The default is 0.
+
+\item \texttt{T0}: prior precision of the subject abilities
+(ideal points). The default is 0.
+
+\item \texttt{ab0}: prior mean of $(\alpha, \beta)$. It can be a scalar or
+a vector of length 2. If it takes a scalar value, then the prior means for
+both $\alpha$ and $\beta$ will be set to that value. The default is 0. 
+
+\item \texttt{AB0}: prior precision of $(\alpha, \beta)$. It can be 
+a scalar or a $2 \times 2$ matrix. If it takes a scalar value, 
+then the prior precision will be \texttt{diag(AB0,2)}. The prior precision
+is assumed to be same for all the items. The default is 0.25.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCirt1d)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(SupremeCourt)
+> names(SupremeCourt) <- c("Rehnquist","Stevens","OConnor","Scalia",
+                         "Kennedy","Souter","Thomas","Ginsburg","Breyer")
+\end{verbatim}
+Fitting a one-dimensional item response theory model using \texttt{irt1d}:
+\begin{verbatim}
+> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
+                       Souter, Thomas, Ginsburg, Breyer) ~ NULL,
+               data = SupremeCourt, model = "irt1d",
+               B0.alpha = 0.2, B0.beta = 0.2, burnin = 500, mcmc = 10000,
+               thin = 20, verbose = TRUE)
+\end{verbatim}
+
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for
+$i=1, \ldots, n$. The choice $Y_{ij}$ is assumed to be determined by
+an unobserved utility $Z_{ij}$, which is a function of the subject $i$'s
+abilities (ideal points) $\theta_i$ and item parameters $\alpha_j$ and
+$\beta_j$ as follows:  
+\begin{eqnarray*}
+Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
+\end{eqnarray*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{ij}  &  \sim & \textrm{Bernoulli}(\pi_{ij}) \\
+& = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
+
+The error term in the unobserved utility equation is independently
+and identically distributed with 
+\begin{eqnarray*}
+\epsilon_{ij} \sim \textrm{Normal}(0, 1).
+\end{eqnarray*}
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\pi_{ij}= \Phi(-\alpha_j + \beta_j' \theta_i),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+normal distribution with mean 0 and variance 1, $\theta_i$ is the
+subject ability (ideal point) parameter, and $\alpha_j$ and $\beta_j$
+are the item parameters. Both subject abilities and item parameters
+are estimated from the model, such that the model is identified by
+placing constraints on the subject ability parameters.
+
+\item The \emph{prior} for $\theta_i$ is given by
+\begin{eqnarray*}
+\theta_i &\sim& \textrm{Normal} \left(  t_{0},T_{0}^{-1}\right)
+\end{eqnarray*}
+
+\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
+\begin{eqnarray*}
+(\alpha_j, \beta_j)' &\sim& \textrm{Normal}\left(  ab_{0},AB_{0}^{-1}\right)
+\end{eqnarray*}
+where $ab_0$ is a 2-vector of prior means and $AB_0$ is a $2 \times 2$ prior
+precision matrix.
+
+\end{itemize}
+ 
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irt1d", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view 
+a default summary of information through \texttt{summary(z.out)}. 
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated subject abilities(ideal points). If
+\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and
+$\beta$ are also contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.   
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for item response models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The unidimensional item-response \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{irt1d} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/models/irtkd.aux b/inst/doc/models/irtkd.aux
new file mode 100644
index 0000000..c037734
--- /dev/null
+++ b/inst/doc/models/irtkd.aux
@@ -0,0 +1,46 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.13}\texttt  {irtkd}: $k$-Dimensional Item Response Theory Model}{189}{section.12.13}}
+\newlabel{irtkd}{{12.13}{189}{\texttt {irtkd}: $k$-Dimensional Item Response Theory Model\relax }{section.12.13}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{189}{section*.224}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{189}{section*.225}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional arguments}{189}{section*.226}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{191}{section*.227}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{191}{section*.228}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{192}{section*.229}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{193}{section*.230}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{193}{section*.231}}
+\@setckpt{models/irtkd}{
+\setcounter{page}{194}
+\setcounter{equation}{1}
+\setcounter{enumi}{1}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{13}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{229}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
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+}
diff --git a/inst/doc/models/irtkd.tex b/inst/doc/models/irtkd.tex
new file mode 100644
index 0000000..0cc6a95
--- /dev/null
+++ b/inst/doc/models/irtkd.tex
@@ -0,0 +1,252 @@
+\section{\texttt{irtkd}: $k$-Dimensional Item Response Theory Model}
+
+\label{irtkd}
+
+Given several observed dependent variables and an unobserved
+explanatory variable, item response theory estimates the latent
+variable (ideal points).  The model is estimated using the Markov
+Chain Monte Carlo algorithm, via a combination of Gibbs sampling and
+data augmentation.  Use this model if you believe that the ideal
+points lie in $k$ dimensions.  See the unidimensional item response
+model (\Sref{irt1d}) for a single hypothesized latent variable. 
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, dimensions = 1, 
+                 model = "irtkd", data = mydata)
+\end{verbatim}
+
+\subsubsection{Inputs}
+\texttt{irtkd} accepts the following arguments:
+\begin{itemize}
+\item \texttt{Y1}, {\tt Y2}, and \texttt{Y3}: \texttt{Y1} contains the items for 
+subject ``Y1'', \texttt{Y2} contains the items for subject ``Y2'', and so on. 
+
+\item \texttt{dimensions}: The number of dimensions in the latent space. The 
+default is 1.
+
+\end{itemize}
+
+\subsubsection{Additional arguments}
+
+\texttt{irtkd} accepts the following additional arguments for model specification:
+\begin{itemize}
+\item \texttt{item.constraints}: a list of lists specifying possible 
+simple equality or inequality constraints on the item parameters.
+A typical entry has one of the following forms: 
+\begin{itemize}
+
+\item {\tt varname = list()}: by default, no constraints are
+imposed.
+
+\item \texttt{varname = list(d, c)}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be equal
+to \texttt{c}.
+
+\item \texttt{varname = list(d, "+")}: constrains the
+$d$th item parameter for the item  named \texttt{varname} to be positive;
+
+\item \texttt{varname = list(d, "-")}: constrains the
+$d$th item parameter for the item named \texttt{varname} to be negative.
+\end{itemize} 
+In a $k$ dimensional model, the first item parameter for item $i$ is
+the difficulty parameter $\alpha_i$, the second item parameter is the
+discrimination parameter on dimension 1, $(\beta_{i,1})$, the third
+item parameter is the discrimination parameter on dimension 2,
+$(\beta_{i,2}),\ldots$, and $(k+1)$th item parameter is the
+discrimination parameter on dimension $k$, $(\beta_{i,k})$. The item
+difficulty parameter($\alpha$) should not be constrained in general.
+\end{itemize}
+
+\noindent \texttt{irtkd} accepts the following additional arguments 
+to monitor the sampling scheme for the Markov chain:
+
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000). 
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 20,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen. The default 
+is \texttt{FALSE}.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{alphabeta.start}: starting values for the item parameters
+$\alpha$ and $\beta$, either a scalar or a $(k+1) \times items$
+matrix. If it is a scalar, then that value will be the starting value
+for all the elements of \texttt{alphabeta.start}. The default is
+\texttt{NA} which sets the starting values for the unconstrained
+elements based on a series of proportional odds logistic
+regressions. The starting values for the inequality constrained
+elements are set to be either 1.0 or -1.0 depending on the nature of
+the constraints.
+
+\item \texttt{store.item}: defaults to {\tt FALSE}.  If {\tt TRUE} stores the
+posterior draws of the item parameters.  (For a large number of draws or
+a large number observations, this may take a lot of memory.)  
+
+\item \texttt{store.ability}: defaults to {\tt TRUE}, storing the
+posterior draws of the subject abilities.  (For a large number of draws or
+a large number observations, this may take a lot of memory.) 
+
+\item \texttt{drop.constant.items}: defaults to {\tt TRUE}, dropping
+items with no variation before fitting the model.  
+\end{itemize}
+
+\noindent \texttt{irtkd} accepts the following additional arguments to 
+specify prior parameters used in the model:
+
+\begin{itemize}
+
+\item \texttt{b0}: prior mean of $(\alpha, \beta)$, either as a scalar or
+a vector of compatible length. If a scalar value, then the prior means
+for both $\alpha$ and $\beta$ will be set to that value. The default
+is 0.
+
+\item \texttt{B0}: prior precision for $(\alpha, \beta)$, either a
+scalar or a $(k+1) \times items$ matrix. If a scalar value, the prior
+precision will be a blocked diagonal matrix with elements
+\texttt{diag(B0,items)}. The prior precision is assumed to be same for
+all the items. The default is 0.25.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCirtKd)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(SupremeCourt)
+> names(SupremeCourt) <- c("Rehnquist","Stevens","OConnor","Scalia",
+                         "Kennedy","Souter","Thomas","Ginsburg","Breyer")
+\end{verbatim}
+Fitting a one-dimensional item response theory model using \texttt{irtkd}:
+\begin{verbatim}
+> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy, Souter, 
+                       Thomas, Ginsburg, Breyer) ~ NULL, dimensions = 1,
+                 data = SupremeCourt, model = "irtkd", B0 = 0.25, 
+                 burnin = 5000, mcmc = 50000, 
+                 thin = 10, verbose = TRUE)
+\end{verbatim}
+
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim} %$
+\end{enumerate} 
+
+\subsubsection{Model} 
+
+Let $Y_i$ be a vector of choices on $J$ items made by subject $i$ for 
+$i = 1, \ldots, n$. The choice $Y_{ij}$ is assumed to be 
+determined by unobserved utility $Z_{ij}$, which is a function
+of subject abilities (ideal points) $\theta_i$ and item parameters
+$\alpha_j$ and $\beta_j$,
+\begin{eqnarray*}
+Z_{ij} &=& -\alpha_j + \beta_j' \theta_i + \epsilon_{ij}.
+\end{eqnarray*}
+In the $k$-dimensional item response theory model, each subject's ability is 
+represented by a $k$-vector, $\theta_i$. Each item has a difficulty 
+parameter $\alpha_j$ and a $k$-dimensional discrimination parameter
+$\beta_j$. In one-dimensional item response theory model, $k = 1$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{ij}  &  \sim & \textrm{Bernoulli}(\pi_{ij})\\
+&  = & \pi_{ij}^{Y_{ij}}(1-\pi_{ij})^{1-Y_{ij}},
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_{ij}=1)=E(Z_{ij})$.
+
+The error term in the unobserved utility equation has
+a standard normal distribution,
+\begin{eqnarray*}
+\epsilon_{ij} \sim \textrm{Normal}(0, 1).
+\end{eqnarray*}
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{ij} &=& \Phi(-\alpha_j + \beta_j \theta_i),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+normal distribution with mean 0 and variance 1, while $\theta_i$
+contains the $k$-dimensional subject abilities(ideal points), and
+$\alpha_{j}$ and $\beta_j$ are the item parameters. Both subject
+abilities and item parameters need to estimated from the model. The
+model is identified by placing constraints on the item parameters.
+
+\item The \emph{prior} for $\theta_i$ is given by
+\begin{eqnarray*}
+\theta_i &\sim& \textrm{Normal}_k(0, I_k)
+\end{eqnarray*}
+
+\item The joint \emph{prior} for $\alpha_j$ and $\beta_j$ is given by
+\begin{eqnarray*}
+(\alpha_j, \beta_j)' &\sim& \textrm{Normal}_{k+1} \left( b_{0_j}, B_{0_j}^{-1}\right)
+\end{eqnarray*}
+where $b_{0_j}$ is a $(k+1)$-vector of prior mean and $B_{0_j}$ is a $(k+1) 
+\times (k+1)$ prior precision matrix which is assumed to be diagonal.
+
+\end{itemize}
+ 
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(cbind(Y1, Y2, Y3) ~ NULL, model = "irtkd", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view 
+a default summary of information through \texttt{summary(z.out)}. 
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated subject abilities(ideal points). If 
+\texttt{store.item = TRUE}, the estimated item parameters $\alpha$ and $\beta$
+ are also contained in \texttt{coefficients}.
+
+\item \texttt{data}: the name of the input data frame.
+\item \texttt{seed}: the random seed used in the model.   
+
+\end{itemize}
+
+\item Since there are no explanatory variables, the \texttt{sim()}
+procedure is not applicable for item response models.
+
+\end{itemize}
+
+\subsubsection{Contributors}
+The $k$ dimensional item response \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{irtkd} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/logit.aux b/inst/doc/models/logit.aux
new file mode 100644
index 0000000..3714fd8
--- /dev/null
+++ b/inst/doc/models/logit.aux
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+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.14}{\tt  logit}: Logistic Regression for Dichotomous Dependent Variables}{194}{section.12.14}}
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+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{194}{section*.232}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{194}{section*.233}}
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+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{199}{section*.239}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{199}{section*.240}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{200}{section*.241}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{200}{section*.242}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{201}{section*.243}}
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+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{202}{section*.245}}
+\citation{MarQui05}
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diff --git a/inst/doc/models/logit.bayes.tex b/inst/doc/models/logit.bayes.tex
new file mode 100644
index 0000000..6b1880a
--- /dev/null
+++ b/inst/doc/models/logit.bayes.tex
@@ -0,0 +1,238 @@
+\section{\texttt{logit.bayes}: Bayesian Logistic Regression}
+
+\label{logit.bayes}
+
+Logistic regression specifies a dichotomous dependent variable as a
+function of a set of explanatory variables using a random walk
+Metropolis algorithm.  For a maximum likelihood implementation, see
+\Sref{logit}.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the Markov chain: 
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000). 
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: Metropolis tuning parameter, either 
+a positive scalar or a vector of length $k$, where $k$ is the number
+of coefficients. The tuning parameter should be set such that the
+acceptance rate of the Metropolis algorithm is satisfactory (typically
+between 0.20 and 0.5) before using the posterior density for
+inference. The default value is 1.1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE}, the progress 
+ of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator.  The
+default is \texttt{NA} which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the maximum
+likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric 
+vector or a scalar. If a scalar value, that value will
+be the prior mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0, which leads to an improper prior.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(logit.bayes)} for more information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(turnout)
+\end{verbatim}
+Estimating the logistic regression using \texttt{logit.bayes}:
+\begin{verbatim}
+> z.out < - zelig(vote ~ race + educate, model = "logit.bayes",
+                  data = turnout, verbose = TRUE)
+\end{verbatim}
+Convergence diagnostics before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim} %$
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given 
+\texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in individual's probability of
+voting  when education is set to be low (25th percentile) versus 
+high (75th percentile) while all the other variables held at their 
+default values.
+\begin{verbatim}
+> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the binary dependent variable for observation $i$ which takes
+the value of either 0 or 1.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i}  &  \sim & \rm{Bernoulli}(\pi_{i})\\
+&  = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
+\end{eqnarray*}
+where $\pi_{i}=\Pr(Y_{i}=1)$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{i}= \frac{1}{1+\exp(-x_{i} \beta)},
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left(  b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the logit model are
+simulations of the predicted probability of a success:
+\begin{eqnarray*}
+E(Y) = \pi_{i}= \frac{1}{1 + \exp(-x_{i} \beta)},
+\end{eqnarray*}
+given the posterior draws of $\beta$ from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
+distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference (\texttt{qi\$fd}) for the logit model is defined
+as
+\begin{eqnarray*}
+\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr})is defined as
+\begin{eqnarray*}
+\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "logit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values(probabilities) for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
+simulated from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian logistic regression \input{models/contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{logit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/logit.tex b/inst/doc/models/logit.tex
new file mode 100644
index 0000000..b4a4a48
--- /dev/null
+++ b/inst/doc/models/logit.tex
@@ -0,0 +1,270 @@
+\section{{\tt logit}: Logistic Regression for Dichotomous Dependent
+Variables}\label{logit}
+
+Logistic regression specifies a dichotomous dependent variable as a
+function of a set of explanatory variables.  For a Bayesian
+implementation, see \Sref{logit.bayes}.  
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "logit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for logistic regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}).  The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame)  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+\begin{enumerate}
+\item {Basic Example}
+ 
+Attaching the sample turnout dataset:
+\begin{verbatim}
+> data(turnout)
+\end{verbatim}
+Estimating parameter values for the logistic regression:
+\begin{verbatim}
+> z.out1 <- zelig(vote ~ race + educate,  model = "logit", data = turnout) 
+> summary(z.out1)
+\end{verbatim}
+Setting values for the explanatory variables to their default values:
+\begin{verbatim}
+> x.out1 <- setx(z.out1)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution.
+\begin{verbatim}
+> s.out1 <- sim(z.out1, x = x.out)
+> summary(s.out1)
+> plot(s.out1)
+\end{verbatim}
+
+\item {Simulating First Differences}
+
+Estimating the risk difference (and risk ratio) between low education
+(25th percentile) and high education (75th percentile) while all the
+other variables held at their default values.
+\begin{verbatim}
+> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+\end{verbatim}
+
+\begin{verbatim}
+> s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
+> summary(s.out2)
+> plot(s.out2)
+\end{verbatim}
+
+\item {Presenting Results: An ROC Plot}  \label{ROC}
+  
+  One can use an ROC plot to evaluate the fit of alternative model
+  specifications.  (Use {\tt demo(roc)} to view this example, or see
+  King and Zeng (2002)\nocite{KinZen02}.)  
+\begin{verbatim}
+> z.out1 <- zelig(vote ~ race + educate + age, model = "logit", 
+                  data = turnout)
+> z.out2 <- zelig(vote ~ race + educate, model = "logit", data = turnout) 
+> rocplot(z.out1, z.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the binary dependent variable for observation $i$ which
+takes the value of either 0 or 1.
+\begin{itemize}
+
+\item The \emph{stochastic component} is given by  
+\begin{eqnarray*}
+Y_i &\sim& \textrm{Bernoulli}(y_i \mid \pi_i) \\
+    &=& \pi_i^{y_i} (1-\pi_i)^{1-y_i}
+\end{eqnarray*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is given by: 
+\begin{equation*}
+\pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+\end{equation*}
+where $x_i$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the logit model are
+  simulations of the predicted probability of a success: $$E(Y) =
+  \pi_i= \frac{1}{1 + \exp(-x_i \beta)},$$ given draws of $\beta$ from
+  its sampling distribution.
+
+\item The predicted values ({\tt qi\$pr}) are draws from the Binomial
+  distribution with mean equal to the simulated expected value $\pi_i$.  
+
+\item The first difference ({\tt qi\$fd}) for the logit model is defined as
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) \ / \ \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)}\right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  Variation in
+    the simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\, x,
+  model = "logit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: the vector of fitted values for the
+     systemic component, $\pi_i$.
+   \item {\tt linear.predictors}: the vector of $x_{i}\beta$
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt data}: the name of the input data frame.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected probabilities for the
+     specified values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values for the
+     specified values of {\tt x}.
+   \item {\tt qi\$fd}: the simulated first difference in the expected
+     probabilities for the values specified in {\tt x} and {\tt x1}.
+   \item {\tt qi\$rr}: the simulated risk ratio for the expected
+     probabilities simulated from {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The logit function is part of the base package by William N. Venables
+and Brian D. Ripley.  Please cite this model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to
+\texttt{help(glm)} and \texttt{help(family)}, as well as
+\begin{verse}
+\bibentry{McCNel89}.
+\end{verse}
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are a selection of $2,000$ observations from 
+\begin{verse}
+\bibentry{KinTomWit00}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/lognormal.aux b/inst/doc/models/lognormal.aux
new file mode 100644
index 0000000..133fe3b
--- /dev/null
+++ b/inst/doc/models/lognormal.aux
@@ -0,0 +1,46 @@
+\relax 
+\citation{Huber81}
+\citation{White80}
+\@writefile{toc}{\contentsline {section}{\numberline {12.16}{\tt  lognorm}: Log-Normal Regression for Duration Dependent Variables}{204}{section.12.16}}
+\newlabel{lognorm}{{12.16}{204}{{\tt lognorm}: Log-Normal Regression for Duration Dependent Variables\relax }{section.12.16}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{204}{section*.247}}
+\@writefile{toc}{\contentsline {subsubsection}{Input Values}{204}{section*.248}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{205}{section*.249}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{205}{section*.250}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{206}{section*.251}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{207}{section*.252}}
+\citation{VenRip02}
+\citation{KinAltBur90}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{208}{section*.253}}
+\@setckpt{models/lognormal}{
+\setcounter{page}{209}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{16}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{234}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/lognormal.tex b/inst/doc/models/lognormal.tex
new file mode 100644
index 0000000..97649ea
--- /dev/null
+++ b/inst/doc/models/lognormal.tex
@@ -0,0 +1,262 @@
+\section{{\tt lognorm}: Log-Normal Regression for Duration
+Dependent Variables}\label{lognorm}
+
+The log-normal model describes an event's duration, the dependent
+variable, as a function of a set of explanatory variables.  The
+log-normal model may take time censored dependent variables, and
+allows the hazard rate to increase and decrease.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X, model = "lognorm", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Log-normal models require that the dependent variable be in the form
+{\tt Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example) of each subject, and the associated
+$c_i$ is a binary variable such that $c_i = 1$ if the duration is not
+censored ({\it e.g.}, the subject dies during the study) or $c_i = 0$
+if the duration is censored ({\it e.g.}, the subject is still alive at
+the end of the study).  If $c_i$ is omitted, all Y are assumed to be
+completed; that is, time defaults to 1 for all observations.
+
+\subsubsection{Input Values} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for lognormal regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
+cluster}.
+\item {\tt cluster}:  if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations.  Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata.  Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3", 
+                 model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters.  If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.  
+\end{itemize}  
+
+\subsubsection{Example}
+
+Attach the sample data: 
+\begin{verbatim}
+> data(coalition)
+\end{verbatim}
+Estimate the model: 
+\begin{verbatim}
+> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "lognorm",
+                 data = coalition)
+\end{verbatim}
+View the regression output:  
+\begin{verbatim}
+> summary(z.out)
+\end{verbatim}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{verbatim}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{verbatim}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt
+  qi\$fd}):
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+
+Let $Y_i^*$ be the survival time for observation $i$ with the density
+function $f(y)$ and the corresponding distribution function
+$F(t)=\int_{0}^t f(y) dy$. This variable might be censored for some
+observations at a fixed time $y_c$ such that the fully observed
+dependent variable, $Y_i$, is defined as
+\begin{equation*}
+  Y_i = \left\{ \begin{array}{ll}
+      Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+      y_c & \textrm{if }Y_i^* > y_c \\
+    \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+  of the partially observed variable, $Y^*$.  For the lognormal model,
+there are two equivalent representations:  
+  \begin{eqnarray*}
+    Y_i^* \; \sim \; \textrm{LogNormal}(\mu_i, \sigma^2) & \textrm{ or
+} & \log(Y_i^*) \; \sim \; \textrm{Normal}(\mu_i, \sigma^2)
+\end{eqnarray*}
+where the parameters $\mu_i$ and $\sigma^2$ are the mean and variance
+of the Normal distribution. (Note that the output from {\tt zelig()}
+parameterizes {\tt scale}$ = \sigma$.)
+
+ In addition, survival models like the lognormal have three additional
+properties. The hazard function $h(t)$ measures the probability of not surviving
+  past time $t$ given survival up to $t$. In general, the hazard
+  function is equal to $f(t)/S(t)$ where the survival function $S(t) =
+  1 - \int_{0}^t f(s) ds$ represents the fraction still surviving at
+  time $t$.  The cumulative hazard function $H(t)$ describes the
+  probability of dying before time $t$.  In general, $H(t)=
+\int_{0}^{t} h(s) ds = -\log S(t)$. In the case of the lognormal model, 
+\begin{eqnarray*}
+h(t) &=& \frac{1}{\sqrt{2 \pi} \, \sigma t \, S(t)}
+\exp\left\{-\frac{1}{2 \sigma^2} (\log \lambda t)^2\right\} \\
+S(t) &=& 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \\
+H(t) &=& -\log \left\{ 1 - \Phi\left(\frac{1}{\sigma} \log \lambda t\right) \right\}
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function for the Normal
+distribution.  
+
+\item The \emph{systematic component} is described as: 
+\begin{equation*}
+\mu_i = x_i \beta .
+\end{equation*}
+
+\end{itemize}
+
+\subsubsection{Quantities of Interest} 
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the lognormal model are
+  simulations of the expected duration:  
+\begin{equation*}
+E(Y) =  \exp\left(\mu_i + \frac{1}{2}\sigma^2 \right),
+\end{equation*}
+given draws of $\beta$ and $\sigma$ from their sampling distributions. 
+
+\item The predicted value is a draw from the log-normal distribution
+  given simulations of the parameters $(\lambda_i, \sigma)$.
+
+\item The first difference ({\tt qi\$fd}) is
+\begin{equation*}
+\textrm{FD} = E(Y \mid x_1) - E(Y \mid x). 
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is \begin{equation*}
+  \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \{ Y_i(t_i=1) - E[Y_i(t_i=0)] \}, \end{equation*} where $t_i$ is a binary explanatory variable
+  defining the treatment ($t_i=1$) and control ($t_i=0$) groups. When
+  $Y_i(t_i=1)$ is censored rather than observed, we replace it with a
+  simulation from the model given available knowledge of the censoring
+  process.  Variation in the simulations is due to two factors:
+  uncertainty in the imputation process for censored $y_i^*$ and
+  uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
+  expected value of $Y_i$ for observations in the treatment group,
+  under the assumption that everything stays the same except that the
+  treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+\begin{equation*}
+  \frac{1}{\sum_{i=1}^n t_i} \sum_{i:t_i=1}^n \{  Y_i(t_i=1) -
+\widehat{Y_i(t_i=0)} \}, 
+\end{equation*} 
+where $t_i$ is a binary explanatory
+  variable defining the treatment ($t_i=1$) and control ($t_i=0$)
+  groups.  When $Y_i(t_i=1)$ is censored rather than observed, we
+  replace it with a simulation from the model given available
+  knowledge of the censoring process.  Variation in the simulations
+  are due to two factors: uncertainty in the imputation process for
+  censored $y_i^*$ and uncertainty in simulating
+  $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$
+  for observations in the treatment group, under the assumption that
+  everything stays the same except that the treatment indicator is
+  switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(Surv(Y,
+  C) \~\, X, model = "lognorm", data)}, then you may examine the
+available information in \texttt{z.out} by using
+\texttt{names(z.out)}, see the {\tt coefficients} by using {\tt
+  z.out\$coefficients}, and a default summary of information
+through \texttt{summary(z.out)}.  Other elements available through
+the {\tt \$} operator are listed below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt icoef}: parameter estimates for the intercept and $\sigma$.  
+   \item {\tt var}: Variance-covariance matrix.  
+   \item {\tt loglik}: Vector containing the log-likelihood for the
+     model and intercept only (respectively).
+   \item {\tt linear.predictors}: the vector of
+     $x_{i}\beta$.
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item Most of this may be conveniently summarized using {\tt
+   summary(z.out)}.  From {\tt summary(z.out)}, you may
+ additionally extract: 
+   \begin{itemize}
+   \item {\tt table}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values for the specified
+     values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distribution defined by $(\lambda_i, \sigma)$.
+   \item {\tt qi\$fd}: the simulated first differences between the
+     simulated expected values for {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The exponential function is part of the survival library by by Terry
+Therneau, ported to R by Thomas Lumley. Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library, and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinAltBur90}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality. 
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
diff --git a/inst/doc/models/ls.aux b/inst/doc/models/ls.aux
new file mode 100644
index 0000000..31f0d21
--- /dev/null
+++ b/inst/doc/models/ls.aux
@@ -0,0 +1,51 @@
+\relax 
+\citation{Zeileis04}
+\citation{Huber81}
+\citation{White80}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.17}{\tt  ls}: Least Squares Regression for Continuous Dependent Variables}{209}{section.12.17}}
+\newlabel{ls}{{12.17}{209}{{\tt ls}: Least Squares Regression for Continuous Dependent Variables\relax }{section.12.17}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{209}{section*.254}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{209}{section*.255}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{209}{section*.256}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{211}{section*.257}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{211}{section*.258}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{211}{section*.259}}
+\citation{VenRip02}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{212}{section*.260}}
+\citation{KinTomWit00}
+\@setckpt{models/ls}{
+\setcounter{page}{214}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
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+\setcounter{chapter}{12}
+\setcounter{section}{17}
+\setcounter{subsection}{0}
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+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
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+\setcounter{Item}{236}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/ls.tex b/inst/doc/models/ls.tex
new file mode 100644
index 0000000..4bc67cc
--- /dev/null
+++ b/inst/doc/models/ls.tex
@@ -0,0 +1,241 @@
+\section{{\tt ls}: Least Squares Regression for Continuous
+Dependent Variables}
+\label{ls}
+
+Use least squares regression analysis to estimate the best linear
+predictor for the specified dependent variables.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "ls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}  
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for least squares regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors based on
+sandwich estimators (see \cite{Zeileis04}, \cite{Huber81}, and
+\cite{White80}).  The default type of robust standard error is
+heteroskedastic consistent (HC), \emph{not} heteroskedastic and
+autocorrelation consistent (HAC).  
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  choose from 
+\begin{itemize}
+\item {\tt "vcovHC"}: (the default if {\tt robust = TRUE}), HC standard errors.
+\item {\tt "vcovHAC"}: HAC standard errors without weights.  
+\item {\tt "kernHAC"}: HAC standard errors using the weights given in
+\cite{Andrews91}.   
+\item {\tt "weave"}: HAC standard errors using the weights given in
+\cite{LumHea99}.
+\end{itemize} 
+\item {\tt order.by}: only applies to the HAC methods above.  Defaults to
+{\tt NULL} (the observations are chronologically ordered as in the
+original data).  Optionally, you may specify a time index (either as
+{\tt order.by = z}, where {\tt z} exists outside the data frame; or
+as {\tt order.by = \~{}z}, where {\tt z} is a variable in the data
+frame).  The observations are chronologically ordered by the size of
+{\tt z}.
+\item {\tt \dots}:  additional options passed to the functions
+specified in {\tt method}.  See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}\begin{enumerate}
+\item Basic Example with First Differences
+
+Attach sample data:
+\begin{verbatim}
+> data(macro)
+\end{verbatim}
+Estimate model:
+\begin{verbatim}
+> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)
+\end{verbatim}
+Summarize regression coefficients:
+\begin{verbatim}
+> summary(z.out1)
+\end{verbatim}
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) values for the trade variable:
+\begin{verbatim}
+> x.high <- setx(z.out1, trade = quantile(trade, 0.8))
+> x.low <- setx(z.out1, trade = quantile(trade, 0.2))
+\end{verbatim}
+Generate first differences for the effect of high versus low trade on
+GDP:
+\begin{verbatim}
+> s.out1 <- sim(z.out1, x = x.high, x1 = x.low)
+> summary(s.out1)
+> plot(s.out1)
+\end{verbatim}
+
+\item Using Dummy Variables
+
+Estimate a model with fixed effects for each country (see
+\Sref{factors} for help with dummy variables).  Note that you do not
+need to create dummy variables, as the program will automatically
+parse the unique values in the selected variable into discrete levels.  
+\begin{verbatim}
+> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(country), 
+                  model = "ls", data = macro)
+\end{verbatim}   
+Set values for the explanatory variables, using the default mean/mode
+values, with country set to the United States and Japan, respectively:
+\begin{verbatim}
+> x.US <- setx(z.out2, country = "United States")
+> x.Japan <- setx(z.out2, country = "Japan")
+\end{verbatim}   
+Simulate quantities of interest:
+\begin{verbatim}
+> s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
+> plot(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+\begin{itemize}
+\item The \emph{stochastic component} is described by a density
+  with mean $\mu_i$ and the common variance $\sigma^2$
+  \begin{equation*}
+    Y_i \; \sim \; f(y_i \mid \mu_i, \sigma^2).
+  \end{equation*}
+\item The \emph{systematic component} models the conditional mean as
+  \begin{equation*}
+     \mu_i =  x_i \beta
+  \end{equation*} 
+  where $x_i$ is the vector of covariates, and $\beta$ is the vector
+  of coefficients.
+  
+  The least squares estimator is the best linear predictor of a
+  dependent variable given $x_i$, and minimizes the sum of squared
+  residuals, $\sum_{i=1}^n (Y_i-x_i \beta)^2$.  
+\end{itemize}
+
+\subsubsection{Quantities of Interest} 
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+  from the stochastic component,  
+\begin{equation*}
+E(Y) = x_i \beta,\end{equation*}
+given a draw of $\beta$ from its sampling distribution.  
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "ls", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+  \item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: fitted values.
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+  
+\item From {\tt summary(z.out)}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+     \begin{equation*}
+       \hat{\beta} \; = \; \left(\sum_{i=1}^n x_i' x_i\right)^{-1} \sum x_i y_i
+     \end{equation*}
+   \item {\tt sigma}: the square root of the estimate variance of the
+     random error $e$:
+     \begin{equation*}
+       \hat{\sigma} \; = \; \frac{\sum (Y_i-x_i\hat{\beta})^2}{n-k}
+     \end{equation*}
+   \item {\tt r.squared}: the fraction of the variance explained by
+     the model. 
+     \begin{equation*}
+       R^2 \; = \; 1 - \frac{\sum (Y_i-x_i\hat{\beta})^2}{\sum (y_i -
+         \bar{y})^2} 
+     \end{equation*}
+   \item {\tt adj.r.squared}: the above $R^2$ statistic, penalizing
+     for an increased number of explanatory variables.  
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+   
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values for the specified
+     values of {\tt x}.
+   \item {\tt qi\$fd}:  the simulated first differences (or
+     differences in expected values) for the specified values of {\tt
+       x} and {\tt x1}. 
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+   
+\subsubsection{Contributors}
+     
+The least squares regression is part of the stats package by William N.
+Venables and Brian D. Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+\texttt{help(lm)} and \texttt{help(lm.fit)}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinTomWit00}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End: 
+
+
+
diff --git a/inst/doc/models/mlogit.aux b/inst/doc/models/mlogit.aux
new file mode 100644
index 0000000..0bde30b
--- /dev/null
+++ b/inst/doc/models/mlogit.aux
@@ -0,0 +1,45 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.18}{\tt  mlogit}: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}{214}{section.12.18}}
+\newlabel{mlogit}{{12.18}{214}{{\tt mlogit}: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values\relax }{section.12.18}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{214}{section*.261}}
+\@writefile{toc}{\contentsline {subsubsection}{Input Values}{214}{section*.262}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{214}{section*.263}}
+\newlabel{ternary}{{12.18}{214}{Examples\relax }{section*.263}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{216}{section*.264}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{216}{section*.265}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{217}{section*.266}}
+\citation{YeeHas03}
+\citation{KinTomWit00}
+\@writefile{toc}{\contentsline {subsubsection}{Further Information}{218}{section*.267}}
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diff --git a/inst/doc/models/mlogit.bayes.aux b/inst/doc/models/mlogit.bayes.aux
new file mode 100644
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--- /dev/null
+++ b/inst/doc/models/mlogit.bayes.aux
@@ -0,0 +1,45 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.19}\texttt  {mlogit.bayes}: Bayesian Multinomial Logistic Regression}{219}{section.12.19}}
+\newlabel{mlogit.bayes}{{12.19}{219}{\texttt {mlogit.bayes}: Bayesian Multinomial Logistic Regression\relax }{section.12.19}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{219}{section*.268}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{219}{section*.269}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{220}{section*.270}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{221}{section*.271}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{222}{section*.272}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{222}{section*.273}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{223}{section*.274}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{224}{section*.275}}
+\@setckpt{models/mlogit.bayes}{
+\setcounter{page}{225}
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+\setcounter{enumi}{2}
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diff --git a/inst/doc/models/mlogit.bayes.tex b/inst/doc/models/mlogit.bayes.tex
new file mode 100644
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+\section{\texttt{mlogit.bayes}: Bayesian Multinomial Logistic
+Regression}
+
+\label{mlogit.bayes}
+
+Use Bayesian multinomial logistic regression to model unordered
+categorical variables.  The dependent variable may be in the format of
+either character strings or integer values.  The model is estimated
+via a random walk Metropolis algorithm or a slice sampler.  See
+\Sref{mlogit} for the maximum-likelihood estimation of this model.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "mlogit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+{\tt zelig()} accepts the following arguments for {\tt mlogit.bayes}:
+\begin{itemize}
+\item \texttt{baseline}: either a character string or numeric value
+(equal to one of the observed values in the dependent variable)
+specifying a baseline category.  The default value is \texttt{NA}
+which sets the baseline to the first alphabetical or numerical unique
+value of the dependent variable.
+\end{itemize}
+
+The model accepts the following additional arguments to monitor the
+Markov chains:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{mcmc.method}: either {\tt "MH"} or {\tt "slice"}, specifying whether
+to use Metropolis Algorithm or slice sampler. The default value is
+\texttt{"MH"}.
+
+\item \texttt{tune}: tuning parameter for the Metropolis-Hasting step,
+either a scalar or a numeric vector (for $k$ coefficients, enter a $k$
+vector).  The tuning parameter should be set such that the acceptance 
+rate is satisfactory (between 0.2 and 0.5). The default value is 1.1.
+
+\item \texttt{verbose}: defaults to \texttt{FALSE}.
+If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
+printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is 
+\texttt{NA} which corresponds to a random seed of 12345. 
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or a vector (for $k$ coefficients, enter a $k$
+vector). The default is \texttt{NA} where the maximum likelihood
+estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following arguments to specify the priors for the model:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a scalar or
+vector.  If a scalar, that value will be the prior mean for all the
+coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix with the dimensions equal to the number of
+coefficients or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0 which
+leads to an improper prior.
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCmnl)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{verbatim}
+> data(mexico)
+\end{verbatim}
+Estimating multinomial logistics regression using \texttt{mlogit.bayes}:
+\begin{verbatim}
+> z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogit.bayes", 
+               data = mexico)
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim} %$
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution
+given \texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in the probabilities of
+voting different candidates when \texttt{pristr} (the strength of the
+PRI) is set to be weak (equal to 1) versus strong (equal to 3)
+while all the other variables held at their default values.
+
+\begin{verbatim}
+> x.weak <- setx(z.out, pristr = 1)
+> x.strong <- setx(z.out, pristr = 3)
+> s.out2 <- sim(z.out, x = x.strong, x1 = x.weak)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the (unordered) categorical dependent variable for observation 
+$i$ which takes an integer values $j=1, \ldots, J$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by:
+\begin{eqnarray*}
+Y_{i} &\sim& \textrm{Multinomial}(Y_i \mid \pi_{ij}).
+\end{eqnarray*}
+where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1, \ldots, J$.
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\pi_{ij}=\frac{\exp(x_i\beta_j)}{\sum_{k=1}^J \exp(x_i\beta_k)},
+\textrm{ for } j=1,\ldots, J-1,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta_j$ is the vector of coefficient for
+category $j$. Category $J$ is assumed to be the baseline category.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta_j \sim \textrm{Normal}_k\left(  b_{0},B_{0}^{-1}\right) 
+\textrm{ for } j = 1, \ldots, J-1,
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the multinomial logistics
+ regression model are the predicted probability of belonging to each
+ category:
+\begin{eqnarray*}
+\Pr(Y_i=j)=\pi_{ij}=\frac{\exp(x_i \beta_j)}{\sum_{k=1}^J \exp(x_J
+\beta_k)}, \quad \textrm{ for } j=1,\ldots, J-1,
+\end{eqnarray*}
+and 
+\begin{eqnarray*}
+\Pr(Y_i=J)=1-\sum_{j=1}^{J-1}\Pr(Y_i=j)
+\end{eqnarray*}
+given the posterior draws of $\beta_j$ for all categories from the 
+MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are the draws of 
+$Y_i$  from a multinomial distribution whose parameters are the expected 
+values(\texttt{qi\$ev}) computed based on the posterior draws 
+of $\beta$ from the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) in category $j$ for the 
+multinomial logistic model is defined as
+\begin{eqnarray*}
+\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
+\begin{eqnarray*}
+\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "mlogit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated coefficients $\beta$ for each category except the baseline
+category. 
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values(probabilities) of 
+each of the $J$ categories given the specified values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values drawn from the 
+multinomial distribution defined by the expected values(\texttt{qi\$ev})
+given the specified values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values of each of the $J$ categories for the values specified in 
+\texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the 
+expected values of each of the $J$ categories simulated 
+from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian multinomial logistic regression \input{models/contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{mlogit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/models/mlogit.tex b/inst/doc/models/mlogit.tex
new file mode 100644
index 0000000..628a00b
--- /dev/null
+++ b/inst/doc/models/mlogit.tex
@@ -0,0 +1,249 @@
+\section{{\tt mlogit}: Multinomial Logistic Regression for
+Dependent Variables with Unordered Categorical Values}\label{mlogit}
+
+Use the multinomial logit distribution to model unordered categorical
+variables.  The dependent variable may be in the format of either
+character strings or integer values.  See \Sref{mlogit.bayes} for a
+Bayesian version of this model.  
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "mlogit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Input Values}
+
+If the user wishes to use the same formula across all levels, then 
+\verb|formula <- as.factor(Y) ~ X1 + X2| may be used. 
+If the user wants to use different formula for each level then the following 
+syntax should be used:
+\begin{verbatim}
+formulae <- list(list(id(Y, "apples")~ X1, 
+                      id(Y, "bananas")~ X1 + X2)
+\end{verbatim}
+where Y above is supposed to be a factor variable with levels {apples,bananas,oranges}.  
+By default, oranges is the last level and omitted.  (You cannot
+specify a different base level at this time.)  
+For $J$ equations, there must be $J + 1$ levels.
+ 
+\subsubsection{Examples} \label{ternary}
+
+\begin{enumerate}
+
+\item {The same formula for each level}
+
+Load the sample data:  
+\begin{verbatim}
+> data(mexico)
+\end{verbatim}
+Estimate the empirical model:  
+\begin{verbatim}
+> z.out1 <- zelig(as.factor(vote88) ~ pristr + othcok + othsocok, 
+                  model = "mlogit", data = mexico)
+\end{verbatim}
+Set the explanatory variables to their default values, with {\tt pristr}
+(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
+equal to 3 (strong) in the alternative values:  
+\begin{verbatim}
+> x.weak <- setx(z.out1, pristr = 1)
+> x.strong <- setx(z.out1, pristr = 3)
+\end{verbatim}
+Generate simulated predicted probabilities {\tt qi\$ev} and differences in
+the predicted probabilities {\tt qi\$fd}:
+\begin{verbatim}
+> s.out1 <- sim(z.out1, x = x.strong, x1 = x.weak)
+> summary(s.out1)
+\end{verbatim}
+Generate simulated predicted probabilities {\tt qi\$ev} for the
+alternative values:  
+\begin{verbatim}
+> ev.weak <- s.out1$qi$ev + s.out1$qi$fd
+\end{verbatim}
+Plot the differences in the predicted probabilities.  
+\begin{verbatim}
+> library(vcd)
+> ternaryplot(s.out1$qi$ev, pch = ".", col = "blue", 
+              main = "1988 Mexican Presidential Election")
+> ternarypoints(ev.weak, pch = ".", col = "red")
+\end{verbatim}
+
+\item {Different formula for each level}
+
+Estimate the empirical model:  
+\begin{verbatim}
+> z.out2 <- zelig(list(id(vote88, "1") ~ pristr + othcok,
+                       id(vote88, "2") ~ othsocok), 
+                  model = "mlogit", data = mexico)
+\end{verbatim}
+Set the explanatory variables to their default values, with {\tt pristr}
+(for the strength of the PRI) equal to 1 (weak) in the baseline values, and
+equal to 3 (strong) in the alternative values:  
+\begin{verbatim}
+> x.weak <- setx(z.out2, pristr = 1)
+> x.strong <- setx(z.out2, pristr = 3)
+\end{verbatim}
+Generate simulated predicted probabilities {\tt qi\$ev} and differences in
+the predicted probabilities {\tt qi\$fd}:
+\begin{verbatim}
+> s.out1 <- sim(z.out2, x = x.strong, x1 = x.weak)
+> summary(s.out1)
+\end{verbatim}
+Generate simulated predicted probabilities {\tt qi\$ev} for the
+alternative values:  
+\begin{verbatim}
+> ev.weak <- s.out2$qi$ev + s.out2$qi$fd
+\end{verbatim}
+Plot the differences in the predicted probabilities.  
+\begin{verbatim}
+> library(vcd)
+> ternaryplot(s.out2$qi$ev, pch = ".", col = "blue", 
+              main = "1988 Mexican Presidential Election")
+> ternarypoints(ev.weak, pch = ".", col = "red")
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the unordered categorical dependent variable that takes
+one of the values from 1 to $J$, where $J$ is the total number of
+categories.
+
+\begin{itemize}
+\item The stochastic component is given by
+  \begin{equation*}
+    Y_i \; \sim \; \textrm{Multinomial}(y_{i} \mid \pi_{ij}), 
+  \end{equation*}
+  where $\pi_{ij}=\Pr(Y_i=j)$ for $j=1,\dots,J$. 
+  
+\item The systemic component is given by:  
+  \begin{equation*}
+    \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
+      \exp(x_{i}\beta_{k})},
+  \end{equation*}
+  where $x_i$ is the vector of explanatory variables for observation
+  $i$, and $\beta_j$ is the vector of coefficients for category $j$.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the predicted probability
+  for each category:
+\begin{equation*}
+  E(Y) \; = \; \pi_{ij}\; = \; \frac{\exp(x_{i}\beta_{j})}{\sum^{J}_{k = 1}
+      \exp(x_{i}\beta_{k})}.
+\end{equation*}
+
+\item The predicted value ({\tt qi\$pr}) is a draw from the
+  multinomial distribution defined by the predicted probabilities.  
+
+\item The first difference in predicted
+  probabilities ({\tt qi\$fd}), for each category is given by:
+\begin{equation*}
+\textrm{FD}_j = \Pr(Y=j \mid x_1) - \Pr(Y=j \mid x) \quad {\rm for}
+\quad j=1,\dots,J.
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "mlogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: the named vector of coefficients.   
+   \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
+     fitted values.
+   \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
+     predictors $x_i \beta_j$.
+   \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.  
+   \item {\tt df.residual}: the residual degrees of freedom.  
+   \item {\tt df.total}: the total degrees of freedom.
+   \item {\tt rss}: the residual sum of squares.  
+   \item {\tt y}: an $n \times J$ matrix of the dependent variables.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+  \item {\tt coef3}: a table of the coefficients with their associated
+    standard errors and $t$-statistics.
+  \item {\tt cov.unscaled}: the variance-covariance matrix. 
+  \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.  
+\end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as arrays.  Available quantities
+  are:  
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected probabilities for the
+     specified values of {\tt x}, indexed by simulation $\times$
+     quantity $\times$ {\tt x}-observation (for more than one {\tt
+       x}-observation).
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distribution defined by the expected probabilities, indexed by
+     simulation $\times$ {\tt x}-observation.
+   \item {\tt qi\$fd}: the simulated first difference in the predicted
+     probabilities for the values specified in {\tt x} and {\tt x1},
+     indexed by simulation $\times$ quantity $\times$ {\tt
+       x}-observation (for more than one {\tt x}-observation).
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models, 
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Further Information}
+
+The multinomial logit model is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)}
+in the VGAM library.  Additional documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinTomWit00}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/models/mloglm.tex b/inst/doc/models/mloglm.tex
new file mode 100644
index 0000000..998d479
--- /dev/null
+++ b/inst/doc/models/mloglm.tex
@@ -0,0 +1,105 @@
+\section{{\tt mloglm}: Multinomial Log-Linear Regression
+for Contingency Table Models}\label{mloglm}
+
+Log-linear models are for modeling contingency tables, the
+cross-tabulation of discrete individual-level variables.  Contingency
+table models take as the ``unit of analysis'' for the purpose of the
+statistical procedure, the cell of a contingency table.  The
+``dependent variable'' is then the count within each cell, and the
+explanatory variables indicate what categories the cells fall into.
+These models are highly efficient computationally since there are so
+few ``observations,'' but they are asymptotically equivalent to
+logistic regression models run on the unpacked individual level data.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> estimate <- zelig(Y ~ X1 + X2, model = "mloglm", data = mydata)
+> Xval <- setx(estimate)
+> results <- sim(estimate, x = Xval)
+\end{verbatim}
+
+\subsubsection{Examples}
+
+\subsubsection{Model}
+
+\subsubsection{Quantities of Interest}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{estimate <- zelig(y \~\,
+  x, model = "mloglm", data)}, then you may examine the available
+information in \texttt{estimate} by using \texttt{names(estimate)},
+see the {\tt coefficients} by using {\tt estimate\$coefficients}, and
+a default summary of information through \texttt{summary(estimate)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output stored in {\tt estimate}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt deviance}: the residual deviance.
+   \item {\tt fitted.values}: the $n \times m$ matrix of in-sample
+     fitted values.
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt edf}: the effective degrees of freedom.  
+   \item {\tt AIC}: Akaike's An Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt Hessian}: the Hessian matrix.
+   \end{itemize}
+
+\item From {\tt summary(estimate)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output stored in {\tt results}:
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected (or fitted values) for
+     the specified values of {\tt x}.  
+   \item {\tt qi\$rd}: the difference in the expected values (or first
+     difference) for the values specified in {\tt x} and
+     {\tt x1}.
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The multinomial logit model is part of the nnet library by Brian D.
+Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{Ripley1996}.
+\end{verse}
+
+Advanced users may wish to refer to the R-help for
+\texttt{help(multinom)} and 
+\begin{verse}
+\bibentry{VenRip2002}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/negbin.aux b/inst/doc/models/negbin.aux
new file mode 100644
index 0000000..6b445b9
--- /dev/null
+++ b/inst/doc/models/negbin.aux
@@ -0,0 +1,50 @@
+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.20}{\tt  negbin}: Negative Binomial Regression for Event Count Dependent Variables}{225}{section.12.20}}
+\newlabel{negbin}{{12.20}{225}{{\tt negbin}: Negative Binomial Regression for Event Count Dependent Variables\relax }{section.12.20}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{225}{section*.276}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{225}{section*.277}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{225}{section*.278}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{226}{section*.279}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{227}{section*.280}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{227}{section*.281}}
+\citation{VenRip02}
+\citation{McCNel89}
+\citation{Zeileis04}
+\citation{Martin92}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{229}{section*.282}}
+\@setckpt{models/negbin}{
+\setcounter{page}{230}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{20}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{240}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
+\setcounter{lsubsection}{0}
+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
+\setcounter{lsubparagraph}{0}
+\setcounter{lsubsubparagraph}{0}
+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/negbin.tex b/inst/doc/models/negbin.tex
new file mode 100644
index 0000000..917ab1a
--- /dev/null
+++ b/inst/doc/models/negbin.tex
@@ -0,0 +1,237 @@
+\section{{\tt negbin}: Negative Binomial Regression for Event
+Count Dependent Variables}\label{negbin}
+
+Use the negative binomial regression if you have a count of events for
+each observation of your dependent variable.  The negative binomial
+model is frequently used to estimate over-dispersed event count
+models.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "negbin", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for negative binomial regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}).  The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame).  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Load sample data:  
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimate the model:  
+\begin{verbatim}
+> z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
+> summary(z.out)
+\end{verbatim}
+Set values for the explanatory variables to their default mean values:  
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulate fitted values:  
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+Let $Y_i$ be the number of independent events that occur during a
+fixed time period. This variable can take any non-negative integer value.
+
+\begin{itemize}
+\item The negative binomial distribution is derived by letting the
+  mean of the Poisson distribution vary according to a fixed
+  parameter $\zeta$ given by the Gamma distribution. The
+  \emph{stochastic component} is given by
+   \begin{eqnarray*}
+     Y_i \mid \zeta_i & \sim & \textrm{Poisson}(\zeta_i \mu_i),\\
+     \zeta_i & \sim & \frac{1}{\theta}\textrm{Gamma}(\theta).
+   \end{eqnarray*}
+   The marginal distribution of $Y_i$ is then the negative binomial
+   with mean $\mu_i$ and variance $\mu_i + \mu_i^2/\theta$:
+   \begin{eqnarray*}
+   Y_i & \sim & \textrm{NegBin}(\mu_i, \theta), \\
+       & = & \frac{\Gamma (\theta + y_i)}{y! \, \Gamma(\theta)} 
+             \frac{\mu_i^{y_i} \, \theta^{\theta}}{(\mu_i + \theta)^{\theta + y_i}},
+   \end{eqnarray*}
+   where $\theta$ is the systematic parameter of the Gamma
+   distribution modeling $\zeta_i$.  
+
+ \item The \emph{systematic component} is given by
+   \begin{equation*}
+     \mu_i = \exp(x_i \beta)
+   \end{equation*}
+   where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
+   the vector of coefficients.
+ \end{itemize}
+
+\subsubsection{Quantities of Interest}
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) are simulations of the mean
+  of the stochastic component.  Thus, $$E(Y) = \mu_i = \exp(x_i
+  \beta),$$ given simulations of $\beta$.  
+  
+\item The predicted value ({\tt qi\$pr}) drawn from the distribution
+  defined by the set of parameters $(\mu_i, \theta)$.
+
+\item The first difference ({\tt qi\$fd}) is
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
+\end{equation*}
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  Variation in
+    the simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "negbin", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt theta}: the maximum likelihood estimate for the
+     stochastic parameter $\theta$.  
+   \item {\tt SE.theta}: the standard error for {\tt theta}.  
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: a vector of the fitted values for the systemic
+     component $\lambda$.  
+   \item {\tt linear.predictors}: a vector of $x_{i} \beta$.  
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values given the specified
+     values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distribution defined by $(\mu_i, \theta)$.  
+   \item {\tt qi\$fd}: the simulated first differences in the
+     simulated expected values given the specified values of {\tt x}
+     and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The negative binomial model is part of the MASS library by William N.
+Venables and Brian D. Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to {\tt help(glm.nb)} in
+the MASS library and \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{Martin92}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
diff --git a/inst/doc/models/normal.aux b/inst/doc/models/normal.aux
new file mode 100644
index 0000000..f1b48c3
--- /dev/null
+++ b/inst/doc/models/normal.aux
@@ -0,0 +1,50 @@
+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.21}{\tt  normal}: Normal Regression for Continuous Dependent Variables}{230}{section.12.21}}
+\newlabel{normal}{{12.21}{230}{{\tt normal}: Normal Regression for Continuous Dependent Variables\relax }{section.12.21}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{230}{section*.283}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{230}{section*.284}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{230}{section*.285}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{232}{section*.286}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{232}{section*.287}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{233}{section*.288}}
+\citation{VenRip02}
+\citation{McCNel89}
+\citation{Zeileis04}
+\citation{KinTomWit00}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{234}{section*.289}}
+\@setckpt{models/normal}{
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diff --git a/inst/doc/models/normal.bayes.aux b/inst/doc/models/normal.bayes.aux
new file mode 100644
index 0000000..a0cbaea
--- /dev/null
+++ b/inst/doc/models/normal.bayes.aux
@@ -0,0 +1,45 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.22}\texttt  {normal.bayes}: Bayesian Normal Linear Regression}{235}{section.12.22}}
+\newlabel{normal.bayes}{{12.22}{235}{\texttt {normal.bayes}: Bayesian Normal Linear Regression\relax }{section.12.22}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{235}{section*.290}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{235}{section*.291}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{236}{section*.292}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{236}{section*.293}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{237}{section*.294}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{238}{section*.295}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{239}{section*.296}}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{239}{section*.297}}
+\@setckpt{models/normal.bayes}{
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diff --git a/inst/doc/models/normal.bayes.tex b/inst/doc/models/normal.bayes.tex
new file mode 100644
index 0000000..54a9769
--- /dev/null
+++ b/inst/doc/models/normal.bayes.tex
@@ -0,0 +1,230 @@
+\section{\texttt{normal.bayes}: Bayesian Normal Linear Regression}
+\label{normal.bayes}
+
+Use Bayesian regression to specify a continuous dependent variable as
+a linear function of specified explanatory variables.  The model is
+implemented using a Gibbs sampler.  See \Sref{normal} for the
+maximum-likelihood implementation or \Sref{ls} for the ordinary least
+squares variation.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the convergence of the Markov
+chain:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}. If \texttt{TRUE}, the
+progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default
+is \texttt{NA}, which corresponds to a random seed of 12345.
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or vector with length equal to the number 
+of estimated coefficients. The default is \texttt{NA}, which uses the
+least squares estimates as the starting values. 
+
+\end{itemize}
+
+Use the following arguments to specify the model's priors:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will
+be the prior mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior. 
+
+\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
+prior on the variance of the disturbance terms. 
+
+\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
+prior on the variance of the disturbance terms. 
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCregress)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample dataset:
+\begin{verbatim}
+> data(macro)
+\end{verbatim}
+Estimating linear regression using \texttt{normal.bayes}:
+\begin{verbatim}
+> z.out < - zelig(unem ~ gdp + capmob + trade, model = "normal.bayes",
+                  data = macro, verbose = TRUE)
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out) 
+\end{verbatim} $%
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given 
+\texttt{x.out}:
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) trade on GDP:
+\begin{verbatim}
+> x.high <- setx(z.out, trade = quantile(macro$trade, prob = 0.8))
+> x.low <- setx(z.out, trade = quantile(macro$trade, prob = 0.2))
+\end{verbatim}
+Estimating the first difference for the effect of
+high versus low trade on unemployment rate:
+\begin{verbatim}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+\epsilon_{i}  &  \sim & \textrm{Normal}(0, \sigma^2)
+\end{eqnarray*}
+where $\epsilon_{i}=Y_i-\mu_i$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i}= x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are given by
+\begin{eqnarray*}
+\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
+\sigma^{2} & \sim & {\rm InverseGamma}\left( \frac{c_0}{2}, 
+\frac{d_0}{2} \right) 
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory
+variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
+a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape and
+scale parameters for $\sigma^{2}$.  Note that $\beta$ and $\sigma^2$
+are assumed to be \emph{a priori} independent.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the linear regression model are
+calculated as following:
+\begin{eqnarray*}
+E(Y) = x_{i} \beta,
+\end{eqnarray*}
+given posterior draws of $\beta$ based on the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) for the linear regression model 
+is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1} \{
+Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "normal.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a 
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws 
+of the variance $\sigma^2$.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+The Bayesian regression \input{models/contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{normal.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/normal.tex b/inst/doc/models/normal.tex
new file mode 100644
index 0000000..d0f7630
--- /dev/null
+++ b/inst/doc/models/normal.tex
@@ -0,0 +1,273 @@
+\section{{\tt normal}: Normal Regression for Continuous Dependent Variables}
+\label{normal}
+
+The Normal regression model is a close variant of the more standard
+least squares regression model (see \Sref{ls}). Both models specify a
+continuous dependent variable as a linear function of a set of
+explanatory variables.  The Normal model reports maximum likelihood
+(rather than least squares) estimates.  The two models differ only in
+their estimate for the stochastic parameter $\sigma$.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "normal", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for normal regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}).  The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame).  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item Basic Example with First Differences
+
+Attach sample data: 
+\begin{verbatim}
+> data(macro)
+\end{verbatim}
+Estimate model:  
+\begin{verbatim}
+> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "normal", 
+                  data = macro)
+\end{verbatim}
+Summarize of regression coefficients:  
+\begin{verbatim}
+> summary(z.out1)
+\end{verbatim}
+Set explanatory variables to their default (mean/mode) values, with
+high (80th percentile) and low (20th percentile) values for trade: 
+\begin{verbatim} 
+> x.high <- setx(z.out, trade = quantile(macro$trade, 0.8))
+> x.low <- setx(z.out, trade = quantile(macro$trade, 0.2))
+\end{verbatim}
+Generate first differences for the effect of high versus low trade on
+GDP: 
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out1)
+\end{verbatim}
+A visual summary of quantities of interest:  
+\begin{verbatim}
+> plot(s.out1)
+\end{verbatim}
+
+\item Using Dummy Variables
+
+Estimate a model with a dummy variable for each year and country (see
+\ref{factors} for help with dummy variables).  Note that you do not
+need to create dummy variables, as the program will automatically
+parse the unique values in the selected variables into dummy
+variables.    
+\begin{verbatim}
+> z.out2 <- zelig(unem ~ gdp + trade + capmob + as.factor(year) 
+                  + as.factor(country), model = "normal", data = macro)
+\end{verbatim}
+Set values for the explanatory variables, using the default mean/mode
+variables, with country set to the United States and Japan,
+respectively: 
+\begin{verbatim}
+> x.US <- setx(z.out2, country = "United States")
+> x.Japan <- setx(z.out2, country = "Japan")
+\end{verbatim}
+Simulate quantities of interest:  
+\begin{verbatim}
+> s.out2 <- sim(z.out2, x = x.US, x1 = x.Japan)
+> summary(s.out2)
+> plot(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i$ be the continuous dependent variable for observation $i$.
+\begin{itemize}
+\item The \emph{stochastic component} is described by a univariate normal
+  model with a vector of means $\mu_i$ and scalar variance $\sigma^2$:
+  \begin{equation*}
+    Y_i \; \sim \; \textrm{Normal}(\mu_i, \sigma^2). 
+  \end{equation*}
+
+\item The \emph{systematic component} is 
+  \begin{equation*}
+    \mu_i \;= \; x_i \beta,
+  \end{equation*}
+  where $x_i$ is the vector of $k$ explanatory variables and $\beta$ is
+  the vector of coefficients.
+\end{itemize}
+
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+  from the the stochastic component, $$E(Y) = \mu_i = x_i \beta,$$
+  given a draw of $\beta$ from its posterior.  
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the distribution
+  defined by the set of parameters $(\mu_i, \sigma)$.  
+
+\item The first difference ({\tt qi\$fd}) is:
+\begin{equation*}
+\textrm{FD}\; = \;E(Y \mid x_1) -  E(Y \mid x)
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  Variation in
+    the simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "normal", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: fitted values.  For the normal model,
+     these are identical to the {\tt linear predictors}.
+   \item {\tt linear.predictors}: fitted values.  For the normal
+     model, these are identical to {\tt fitted.values}.
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values for the specified
+     values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distribution defined by $(\mu_i, \sigma)$.
+   \item {\tt qi\$fd}: the simulated first difference in the simulated
+     expected values for the values specified in {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Normal model is part of the stats package by William N. Venables
+and Brian D. Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to
+\texttt{help(glm)}, \texttt{help(family)}, and \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinTomWit00}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/ologit.aux b/inst/doc/models/ologit.aux
new file mode 100644
index 0000000..ac9c307
--- /dev/null
+++ b/inst/doc/models/ologit.aux
@@ -0,0 +1,45 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.23}{\tt  ologit}: Ordinal Logistic Regression for Ordered Categorical Dependent Variables}{240}{section.12.23}}
+\newlabel{ologit}{{12.23}{240}{{\tt ologit}: Ordinal Logistic Regression for Ordered Categorical Dependent Variables\relax }{section.12.23}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{240}{section*.298}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{240}{section*.299}}
+\newlabel{ord.fact}{{1}{240}{Example\relax }{Item.245}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{241}{section*.300}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{242}{section*.301}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{242}{section*.302}}
+\citation{VenRip02}
+\citation{McCNel89}
+\citation{Martin92}
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diff --git a/inst/doc/models/ologit.tex b/inst/doc/models/ologit.tex
new file mode 100644
index 0000000..588e5b9
--- /dev/null
+++ b/inst/doc/models/ologit.tex
@@ -0,0 +1,244 @@
+\section{{\tt ologit}: Ordinal Logistic Regression for Ordered
+Categorical Dependent Variables}\label{ologit}
+
+Use the ordinal logit regression model if your dependent variable is
+ordered and categorical, either in the form of integer values or character strings.  
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "ologit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order automatically order the values.  If {\tt Y} takes
+on values composed of character strings, such as ``strongly agree'',
+``agree'', and ``disagree'', {\tt as.factor()} will order the values
+in the order in which they appear in {\tt Y}.  You will need to
+replace your dependent variable with a factored variable prior to
+estimating the model through {\tt zelig()}.  See Section \ref{factors}
+for more information on creating ordered factors and Example
+\ref{ord.fact} below.
+
+\subsubsection{Example}
+
+\begin{enumerate}
+
+\item {Creating An Ordered Dependent Variable} \label{ord.fact}
+
+Load the sample data:  
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Create an ordered dependent variable: 
+\begin{verbatim}
+> sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+                          levels = c("net gain", "little effect", 
+                          "modest loss", "major loss"))
+\end{verbatim}
+Estimate the model:
+\begin{verbatim}
+> z.out <- zelig(ncost ~ mil + coop, model = "ologit", data = sanction)
+\end{verbatim}
+Set the explanatory variables to their observed values:  
+\begin{verbatim}
+> x.out <- setx(z.out, fn = NULL)
+\end{verbatim}
+Simulate fitted values given {\tt x.out} and view the results:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{verbatim}
+
+\item {First Differences}
+
+Load the sample data: 
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimate the empirical model and returning the coefficients:
+\begin{verbatim}
+> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "ologit", 
+                 data = sanction)
+summary(z.out)
+\end{verbatim}
+Set the explanatory variables to their means, with {\tt mil} set
+to 0 (no military action in addition to sanctions) in the baseline
+case and set to 1 (military action in addition to sanctions) in the
+alternative case:
+\begin{verbatim}
+> x.low <- setx(z.out, mil = 0)
+> x.high <- setx(z.out, mil = 1)
+\end{verbatim}
+Generate simulated fitted values and first differences, and view the results:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_i$ be the ordered categorical dependent variable for
+observation $i$ that takes one of the integer values from $1$ to $J$
+where $J$ is the total number of categories.
+  
+\begin{itemize}
+\item The \emph{stochastic component} begins with an unobserved continuous
+  variable, $Y^*_i$, which follows the standard logistic distribution
+  with a parameter $\mu_i$,
+  \begin{equation*}
+    Y_i^* \; \sim \; \textrm{Logit}(y_i^* \mid \mu_i),  
+  \end{equation*}
+  to which we add an observation mechanism
+  \begin{equation*}
+    Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+    \quad {\rm for} \quad j=1,\dots,J.
+  \end{equation*}
+  where $\tau_l$ (for $l=0,\dots,J$) are the threshold parameters with
+  $\tau_l < \tau_m$ for all $l<m$ and $\tau_0=-\infty$ and
+  $\tau_J=\infty$.
+  
+\item The \emph{systematic component} has the following form, given
+  the parameters $\tau_j$ and $\beta$, and the explanatory variables $x_i$: 
+  \begin{equation*}
+    \Pr(Y \le j) \; = \; \Pr(Y^* \le \tau_j) \; = \frac{\exp(\tau_j -
+      x_i \beta)}{1+\exp(\tau_j -x_i \beta)},
+  \end{equation*}
+  which implies:
+  \begin{equation*}
+    \pi_{j}  \; = \; \frac{\exp(\tau_j - x_i \beta)}{1 + \exp(\tau_j -
+      x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+      \exp(\tau_{j-1} - x_i \beta)}.
+  \end{equation*}
+\end{itemize}
+
+\subsubsection{Quantities of Interest} 
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the ordinal logit model
+  are simulations of the predicted probabilities for each category: 
+\begin{equation*}
+E(Y = j) \; = \; \pi_{j} \; = \; \frac{\exp(\tau_j - x_i \beta)}
+{1 + \exp(\tau_j - x_i \beta)} - \frac{\exp(\tau_{j-1} - x_i \beta)}{1 +
+ \exp(\tau_{j-1} - x_i \beta)},
+\end{equation*}
+given a draw of $\beta$ from its sampling distribution.  
+
+\item The predicted value ({\tt qi\$pr}) is drawn from the logit
+  distribution described by $\mu_i$, and observed as one of $J$
+  discrete outcomes.  
+
+\item The difference in each of the predicted probabilities ({\tt
+    qi\$fd}) is given by
+  \begin{equation*}
+    \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+    j=1,\dots,J.
+  \end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{n_j}\sum_{i:t_i=1}^{n_j} \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "ologit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt zeta}: a vector containing the estimated class
+     boundaries $\tau_j$.
+   \item {\tt deviance}: the residual deviance.
+   \item {\tt fitted.values}: the $n \times J$ matrix of in-sample
+     fitted values.
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt edf}: the effective degrees of freedom.  
+   \item {\tt Hessian}: the Hessian matrix.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, and $t$-statistics.
+   \end{itemize}
+   
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+   quantities of interest arranged as arrays.  Available quantities
+   are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected probabilities for the
+     specified values of {\tt x}, indexed by simulation $\times$
+     quantity $\times$ {\tt x}-observation (for more than one {\tt
+       x}-observation).
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distribution defined by the expected probabilities, indexed by
+     simulation $\times$ {\tt x}-observation.
+   \item {\tt qi\$fd}: the simulated first difference in the predicted
+     probabilities for the values specified in {\tt x} and {\tt x1},
+     indexed by simulation $\times$ quantity $\times$ {\tt
+       x}-observation (for more than one {\tt x}-observation).
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The ordinal logit model is part of the MASS library by William N.
+Venables and Brian D. Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to
+{\tt help(polr)} in the MASS libary and \cite{McCNel89}.
+
+Sample data are from 
+\begin{verse}
+\bibentry{Martin92}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
diff --git a/inst/doc/models/oprobit.aux b/inst/doc/models/oprobit.aux
new file mode 100644
index 0000000..15e8aa6
--- /dev/null
+++ b/inst/doc/models/oprobit.aux
@@ -0,0 +1,44 @@
+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.24}{\tt  oprobit}: Ordinal Probit Regression for Ordered Categorical Dependent Variables}{245}{section.12.24}}
+\newlabel{oprobit}{{12.24}{245}{{\tt oprobit}: Ordinal Probit Regression for Ordered Categorical Dependent Variables\relax }{section.12.24}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{245}{section*.304}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{245}{section*.305}}
+\newlabel{ord.fact.p}{{1}{245}{Example\relax }{Item.247}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{246}{section*.306}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{247}{section*.307}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{248}{section*.308}}
+\citation{YeeHas03}
+\citation{Martin92}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{249}{section*.309}}
+\@setckpt{models/oprobit}{
+\setcounter{page}{250}
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diff --git a/inst/doc/models/oprobit.bayes.aux b/inst/doc/models/oprobit.bayes.aux
new file mode 100644
index 0000000..c9cfe4a
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+\relax 
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+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{250}{section*.310}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{250}{section*.311}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{251}{section*.312}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{251}{section*.313}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{252}{section*.314}}
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+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{255}{section*.317}}
+\@setckpt{models/oprobit.bayes}{
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diff --git a/inst/doc/models/oprobit.bayes.tex b/inst/doc/models/oprobit.bayes.tex
new file mode 100644
index 0000000..c2b0657
--- /dev/null
+++ b/inst/doc/models/oprobit.bayes.tex
@@ -0,0 +1,273 @@
+\section{\texttt{oprobit.bayes}: Bayesian Ordered Probit Regression}
+
+\label{oprobit.bayes}
+
+Use the ordinal probit regression model if your dependent variables are ordered and 
+categorical.  They may take either integer values or character strings.  The model 
+is estimated using a Gibbs sampler with data augmentation.  For a 
+maximum-likelihood implementation of this models, see \Sref{oprobit}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "oprobit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+
+\subsubsection{Additional Inputs}
+
+{\tt zelig()} accepts the following arguments to monitor the Markov
+chain:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item{\texttt{tune}}: tuning parameter for the Metropolis-Hasting step.
+The default value is \texttt{NA} which corresponds to 0.05 divided by
+the number of categories in the response variable.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default 
+is \texttt{NA} which corresponds to a random seed 12345.
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or vector with length equal to the number 
+of estimated coefficients. The default is \texttt{NA}, which uses the
+maximum likelihood estimates as the starting values.  
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric 
+vector or a scalar. If a scalar value, that value will be the prior
+mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with dimensions equal to the number of
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0 which leads to an improper prior.
+\end{itemize}
+
+\noindent Zelig users may wish to refer to \texttt{help(MCMCoprobit)} 
+for more information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimating ordered probit regression using \texttt{oprobit.bayes}:
+\begin{verbatim}
+> z.out <- zelig(ncost ~ mil + coop, model = "oprobit.bayes",
+                  data = sanction, verbose=TRUE)
+\end{verbatim}
+
+Creating an ordered dependent variable:
+\begin{verbatim}
+sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+                         levels = c("net gain", "little effect", 
+                         "modest loss", "major loss"))
+\end{verbatim}
+
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out) 
+\end{verbatim} %$ 
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given:
+\texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in the probabilities of
+incurring different level of cost when there is no military action versus 
+military action while all the other variables held at their 
+default values.
+
+\begin{verbatim}
+> x.high <- setx(z.out, mil=0)
+> x.low <- setx(z.out, mil=1)
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_{i}$ be the ordered categorical dependent variable for
+observation $i$ which takes an integer value $j=1, \ldots, J$.
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by an unobserved 
+continuous variable, $Y_i^*$, 
+\begin{eqnarray*}
+Y_{i}^*  \sim \textrm{Normal}(\mu_i, 1).
+\end{eqnarray*}
+Instead of $Y_i^*$, we observe categorical variable $Y_i$,
+\begin{eqnarray*}
+Y_i = j \quad \textrm{ if } \tau_{j-1} \le Y_i^* \le \tau_j \textrm{
+for } j=1,\ldots, J.
+\end{eqnarray*}
+where $\tau_j$ for $j=0,\ldots, J$ are the threshold parameters with
+the following constraints, $\tau_l < \tau_m$ for $l < m$, and
+$\tau_0=-\infty, \tau_J=\infty$.
+
+The probability of observing $Y_i$ equal to category $j$ is,
+\begin{eqnarray*}
+\Pr(Y_i=j) &=& \Phi(\tau_j \mid \mu_i)-\Phi(\tau_{j-1} \mid \mu_i) 
+\textrm{ for } j=1,\ldots, J
+\end{eqnarray*}
+where $\Phi(\cdot \mid \mu_i)$ is the cumulative distribution function
+of the Normal distribution with mean $\mu_i$ and variance 1.
+
+\item The \emph{systematic component} is given by
+
+\begin{eqnarray*}
+\mu_{i}= x_i \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for 
+observation $i$ and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k\left(  b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the ordered probit model are
+the predicted probability of belonging to each category:
+\begin{eqnarray*}
+\Pr(Y_i=j)= \Phi(\tau_j \mid x_i \beta)-\Phi(\tau_{j-1} \mid x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ and threshold parameters $\tau$
+from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are the observed values of 
+$Y_i$ given the observation scheme and the posterior draws of $\beta$
+and cut points $\tau$ from the MCMC iterations.
+
+\item The first difference (\texttt{qi\$fd}) in category $j$ for the 
+ordered probit model is defined as
+\begin{eqnarray*}
+\text{FD}_j=\Pr(Y_i=j\mid X_{1})-\Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr}) in category $j$ is defined as
+\begin{eqnarray*}
+\text{RR}_j=\Pr(Y_i=j\mid X_{1})\ /\ \Pr(Y_i=j\mid X).
+\end{eqnarray*}
+
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j} \{
+Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)] \},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of observations in the treatment group that belong to category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of observations in the treatment group that belong to category $j$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "oprobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior
+distribution of the \texttt{coefficients} by using
+\texttt{z.out\$coefficients}, and view a default summary of
+information through \texttt{summary(z.out)}. Other elements available
+through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated coefficients $\beta$ and threshold parameters $\tau$.
+Note, element $\tau_1$ is normalized to 0 and is not returned in the 
+\texttt{coefficients} object.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values (probabilities) of 
+each of the $J$ categories for the specified values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values (observed values)
+ for the specified values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values of each of the $J$ categories for the values specified in 
+\texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the 
+expected values of each of the $J$ categories simulated 
+from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian ordinal probit regression \input{models/contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{oprobit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/models/oprobit.tex b/inst/doc/models/oprobit.tex
new file mode 100644
index 0000000..e654e40
--- /dev/null
+++ b/inst/doc/models/oprobit.tex
@@ -0,0 +1,252 @@
+\section{{\tt oprobit}: Ordinal Probit Regression for Ordered
+Categorical Dependent Variables}\label{oprobit}
+
+Use the ordinal probit regression model if your dependent variables
+are ordered and categorical.  They may take on either integer values
+or character strings.  For a Bayesian implementation of this model,
+see \Sref{oprobit.bayes}.  
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(as.factor(Y) ~ X1 + X2, model = "oprobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+If {\tt Y} takes discrete integer values, the {\tt as.factor()}
+command will order it automatically.  If {\tt Y} takes on values
+composed of character strings, such as ``strongly agree'', ``agree'',
+and ``disagree'', {\tt as.factor()} will order the values in the order
+in which they appear in {\tt Y}.  You will need to replace your
+dependent variable with a factored variable prior to estimating the
+model through {\tt zelig()}.  See \Sref{factors} for more information
+on creating ordered factors and Example \ref{ord.fact.p} below.
+
+\subsubsection{Example}
+\begin{enumerate}
+\item {Creating An Ordered Dependent Variable} \label{ord.fact.p}
+
+Load the sample data:  
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Create an ordered dependent variable: 
+\begin{verbatim}
+> sanction$ncost <- factor(sanction$ncost, ordered = TRUE,
+                           levels = c("net gain", "little effect", 
+                           "modest loss", "major loss"))
+\end{verbatim}
+Estimate the model:
+\begin{verbatim}
+> z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanction)
+\end{verbatim}
+Set the explanatory variables to their observed values:  
+\begin{verbatim}
+> x.out <- setx(z.out, fn = NULL)
+\end{verbatim}
+Simulate fitted values given {\tt x.out} and view the results:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+
+\item {First Differences}
+
+Load the sample data: 
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimate the empirical model and returning the coefficients:
+\begin{verbatim}
+> z.out <- zelig(as.factor(cost) ~ mil + coop, model = "oprobit", 
+                 data = sanction)
+> summary(z.out)
+\end{verbatim}
+Set the explanatory variables to their means, with {\tt mil} set
+to 0 (no military action in addition to sanctions) in the baseline
+case and set to 1 (military action in addition to sanctions) in the
+alternative case:
+\begin{verbatim}
+> x.low <- setx(z.out, mil = 0)
+> x.high <- setx(z.out, mil = 1)
+\end{verbatim}
+Generate simulated fitted values and first differences, and view the results:
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+  Let $Y_i$ be the ordered categorical dependent variable for
+  observation $i$ that takes one of the integer values from $1$ to $J$
+  where $J$ is the total number of categories.
+\begin{itemize}
+\item The \emph{stochastic component} is described by an unobserved continuous
+  variable, $Y^*_i$, which follows the normal distribution with mean
+  $\mu_i$ and unit variance
+  \begin{equation*}
+    Y_i^* \; \sim \; N(\mu_i, 1). 
+  \end{equation*}
+  The observation mechanism is 
+  \begin{equation*}
+    Y_i \; = \; j \quad {\rm if} \quad \tau_{j-1} \le Y_i^* \le \tau_j
+    \quad {\rm for} \quad j=1,\dots,J.
+  \end{equation*}
+  where $\tau_k$ for $k=0,\dots,J$ is the threshold parameter with the
+  following constraints; $\tau_l < \tau_m$ for all $l<m$ and
+  $\tau_0=-\infty$ and $\tau_J=\infty$.
+  
+  Given this observation mechanism, the probability for each category,
+  is given by
+  \begin{equation*}
+    \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i) - \Phi(\tau_{j-1} \mid
+    \mu_i) \quad {\rm for} \quad j=1,\dots,J
+  \end{equation*}
+  where $\Phi(\mu_i)$ is the cumulative distribution function for the
+  Normal distribution with mean $\mu_i$ and unit variance.
+  
+\item The \emph{systematic component} is given by
+  \begin{equation*}
+    \mu_i \; = \; x_i \beta
+  \end{equation*}
+  where $x_i$ is the vector of explanatory variables and $\beta$ is
+  the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest} 
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the ordinal probit model
+  are simulations of the predicted probabilities for each category:
+\begin{equation*}
+    E(Y_i = j) \; = \; \Pr(Y_i = j) \; = \; \Phi(\tau_{j} \mid \mu_i)
+    - \Phi(\tau_{j-1} \mid  \mu_i) \quad {\rm for} \quad j=1,\dots,J, 
+\end{equation*}
+given draws of $\beta$ from its posterior.
+  
+\item The predicted value ({\tt qi\$pr}) is the observed value of
+  $Y_i$ given the underlying standard normal distribution described by
+  $\mu_i$.
+
+\item The difference in each of the predicted probabilities ({\tt
+    qi\$fd}) is given by
+  \begin{equation*}
+    \Pr(Y=j \mid x_1) \;-\; \Pr(Y=j \mid x) \quad {\rm for} \quad
+    j=1,\dots,J.
+  \end{equation*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group in category $j$ is
+\begin{eqnarray*}
+\frac{1}{n_j}\sum_{i:t_{i}=1}^{n_j}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups, and $n_j$ is the 
+number of treated observations in category $j$.
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "oprobit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: the named vector of coefficients.   
+   \item {\tt fitted.values}: an $n \times J$ matrix of the in-sample
+     fitted values.
+   \item {\tt predictors}: an $n \times (J-1)$ matrix of the linear
+     predictors $x_i \beta_j$.
+   \item {\tt residuals}: an $n \times (J-1)$ matrix of the residuals.  
+   \item {\tt df.residual}: the residual degrees of freedom.  
+   \item {\tt df.total}: the total degrees of freedom.
+   \item {\tt rss}: the residual sum of squares.  
+   \item {\tt y}: an $n \times J$ matrix of the dependent variables.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract:
+\begin{itemize}
+  \item {\tt coef3}: a table of the coefficients with their associated
+    standard errors and $t$-statistics.
+  \item {\tt cov.unscaled}: the variance-covariance matrix. 
+  \item {\tt pearson.resid}: an $n \times (m-1)$ matrix of the Pearson residuals.  
+\end{itemize}
+
+ \item From the {\tt sim()} output object {\tt s.out}, you may extract
+   quantities of interest arranged as arrays.  Available quantities
+   are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected probabilities for the
+     specified values of {\tt x}, indexed by simulation $\times$
+     quantity $\times$ {\tt x}-observation (for more than one {\tt
+       x}-observation).
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distribution defined by the expected probabilities, indexed by
+     simulation $\times$ {\tt x}-observation.
+   \item {\tt qi\$fd}: the simulated first difference in the predicted
+     probabilities for the values specified in {\tt x} and {\tt x1},
+     indexed by simulation $\times$ quantity $\times$ {\tt
+       x}-observation (for more than one {\tt x}-observation).
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The ordinal probit function is part of the VGAM package by Thomas Yee.
+Please cite the model as:
+\begin{verse}
+\bibentry{YeeHas03}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(vglm)} 
+in the VGAM library.  Additional
+documentation is available at
+\hlink{http://www.stat.auckland.ac.nz/\~\,yee}{http://www.stat.auckland.ac.nz/~yee}.
+
+Sample data are from 
+\begin{verse}
+\bibentry{Martin92}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
diff --git a/inst/doc/models/poisson.aux b/inst/doc/models/poisson.aux
new file mode 100644
index 0000000..a133166
--- /dev/null
+++ b/inst/doc/models/poisson.aux
@@ -0,0 +1,50 @@
+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.26}{\tt  poisson}: Poisson Regression for Event Count Dependent Variables}{256}{section.12.26}}
+\newlabel{poisson}{{12.26}{256}{{\tt poisson}: Poisson Regression for Event Count Dependent Variables\relax }{section.12.26}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{256}{section*.318}}
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+\@writefile{toc}{\contentsline {subsubsection}{Example}{256}{section*.320}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{257}{section*.321}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{257}{section*.322}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{258}{section*.323}}
+\citation{VenRip02}
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diff --git a/inst/doc/models/poisson.bayes.aux b/inst/doc/models/poisson.bayes.aux
new file mode 100644
index 0000000..4f9ab69
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+++ b/inst/doc/models/poisson.bayes.aux
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+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.27}\texttt  {poisson.bayes}: Bayesian Poisson Regression}{260}{section.12.27}}
+\newlabel{poisson.bayes}{{12.27}{260}{\texttt {poisson.bayes}: Bayesian Poisson Regression\relax }{section.12.27}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{260}{section*.325}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{260}{section*.326}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{261}{section*.327}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{261}{section*.328}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{262}{section*.329}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{263}{section*.330}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{263}{section*.331}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{264}{section*.332}}
+\@setckpt{models/poisson.bayes}{
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diff --git a/inst/doc/models/poisson.bayes.tex b/inst/doc/models/poisson.bayes.tex
new file mode 100644
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--- /dev/null
+++ b/inst/doc/models/poisson.bayes.tex
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+\section{\texttt{poisson.bayes}: Bayesian Poisson Regression}
+
+\label{poisson.bayes}
+
+Use the Poisson regression model if the observations of your dependent
+variable represents the number of independent events that occur during
+a fixed period of time. The model is fit using a random walk
+Metropolis algorithm.  For a maximum-likelihood estimation of this
+model see \Sref{poisson}.
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Use the following argument to monitor the Markov chain:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000). 
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}:  thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{tune}: Metropolis tuning parameter, either 
+a positive scalar or a vector of length $k$, where $k$ is the number
+of coefficients. The tuning parameter should be set such that the
+acceptance rate of the Metropolis algorithm is satisfactory (typically
+between 0.20 and 0.5). The default value is 1.1.
+
+\item \texttt{verbose}: default to {\tt FALSE}. 
+If \texttt{TRUE}, the progress of the sampler (every $10\%$) is
+printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default 
+is \texttt{NA} which corresponds to a random seed of 12345. 
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the
+maximum likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric 
+vector or a scalar. If a scalar, that value will be the prior mean for
+all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCpoisson)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimating the Poisson regression using \texttt{poisson.bayes}:
+\begin{verbatim}
+> z.out <- zelig(num ~ target + coop, model = "poisson.bayes",
+                  data = sanction, verbose = TRUE)
+\end{verbatim}
+Checking convergence diagnostics before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim} %$
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given 
+\texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Estimating the first difference in the number of countries imposing sanctions
+when the number of targets is set to be its maximum versus its minimum :
+\begin{verbatim}
+> x.max <- setx(z.out, target = max(sanction$target))
+> x.min <- setx(z.out, target = min(sanction$target))
+> s.out2 <- sim(z.out, x = x.max, x1 = x.min)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the number of independent events that occur during 
+a fixed time period. 
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i}  &  \sim & \textrm{Poisson}(\lambda_i)
+\end{eqnarray*}
+where $\lambda_i$ is the mean and variance parameter.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\lambda_{i}= \exp(x_{i} \beta)
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for observation $i$
+and $\beta$ is the vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left(  b_{0},B_{0}^{-1}\right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the Poisson model are
+calculated as following:
+\begin{eqnarray*}
+E(Y\mid X) = \lambda_i = \exp(x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ based on the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Poisson
+distribution with parameter $\lambda_i$.
+
+\item The first difference (\texttt{qi\$fd}) for the Poisson model is defined
+as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid X_{1})-E(Y\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}\{Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]\},
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum_{i=1}^n t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "poisson.bayes", data)
+\end{verbatim}
+
+\noindent you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a 
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian Poisson regression \input{models/contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{poisson.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/models/poisson.tex b/inst/doc/models/poisson.tex
new file mode 100644
index 0000000..65456f4
--- /dev/null
+++ b/inst/doc/models/poisson.tex
@@ -0,0 +1,219 @@
+\section{{\tt poisson}: Poisson Regression for Event Count
+Dependent Variables}\label{poisson}
+
+Use the Poisson regression model if the observations of your dependent
+variable represents the number of independent events that occur during
+a fixed period of time (see the negative binomial model, \Sref{negbin},
+for over-dispersed event counts.)  For a Bayesian implementation of
+this model, see \Sref{poisson.bayes}.  
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "poisson", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for poisson regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}).  The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame).  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Example}
+
+Load sample data:  
+\begin{verbatim}
+> data(sanction)
+\end{verbatim}
+Estimate Poisson model:  
+\begin{verbatim}
+> z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
+> summary(z.out)
+\end{verbatim}
+Set values for the explanatory variables to their default mean values:  
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulate fitted values:  
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+Let $Y_i$ be the number of independent events that occur during a
+fixed time period. This variable can take any non-negative integer.
+
+\begin{itemize}
+\item The Poisson distribution has \emph{stochastic component}
+  \begin{equation*}
+    Y_i \; \sim \; \textrm{Poisson}(\lambda_i),
+  \end{equation*}
+  where $\lambda_i$ is the mean and variance parameter.
+  
+\item The \emph{systematic component} is 
+  \begin{equation*}
+    \lambda_i \; = \; \exp(x_i \beta),
+  \end{equation*}
+  where $x_i$ is the vector of explanatory variables, and $\beta$ is
+  the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+  
+\item The expected value ({\tt qi\$ev}) is the mean of simulations
+  from the stochastic component, $$E(Y) = \lambda_i =  \exp(x_i
+  \beta),$$ given draws of $\beta$ from its sampling distribution.  
+  
+\item The predicted value ({\tt qi\$pr}) is a random draw from the
+  poisson distribution defined by mean $\lambda_i$.
+
+\item The first difference in the expected values ({\tt qi\$fd}) is given by:
+\begin{equation*}
+\textrm{FD} \; = \; E(Y | x_1) - E(Y \mid x)
+\end{equation*}
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  Variation in
+    the simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "poisson", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: a vector of the fitted values for the systemic
+     component $\lambda$.  
+   \item {\tt linear.predictors}: a vector of $x_{i}\beta$.  
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values given the
+     specified values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distributions defined by $\lambda_i$.
+   \item {\tt qi\$fd}: the simulated first differences in the expected
+     values given the specified values of {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Poisson model is part of the stats package by William N. Venables
+and Brian D. Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Advanced users may wish to refer to {\tt help(glm)} and {\tt
+  help(family)}, as well as \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{Martin92}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
diff --git a/inst/doc/models/probit.aux b/inst/doc/models/probit.aux
new file mode 100644
index 0000000..4e1a4e1
--- /dev/null
+++ b/inst/doc/models/probit.aux
@@ -0,0 +1,50 @@
+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\citation{Zeileis04}
+\@writefile{toc}{\contentsline {section}{\numberline {12.28}{\tt  probit}: Probit Regression for Dichotomous Dependent Variables}{265}{section.12.28}}
+\newlabel{probit}{{12.28}{265}{{\tt probit}: Probit Regression for Dichotomous Dependent Variables\relax }{section.12.28}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{265}{section*.333}}
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+\@writefile{toc}{\contentsline {subsubsection}{Model}{266}{section*.336}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{266}{section*.337}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{267}{section*.338}}
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+\citation{Zeileis04}
+\citation{KinTomWit00}
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diff --git a/inst/doc/models/probit.bayes.aux b/inst/doc/models/probit.bayes.aux
new file mode 100644
index 0000000..fa8d4fd
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+++ b/inst/doc/models/probit.bayes.aux
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+\relax 
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+\newlabel{probit.bayes}{{12.29}{270}{\texttt {probit.bayes}: Bayesian Probit Regression\relax }{section.12.29}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{270}{section*.340}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{270}{section*.341}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{271}{section*.342}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{271}{section*.343}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{272}{section*.344}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{273}{section*.345}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{273}{section*.346}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{274}{section*.347}}
+\@setckpt{models/probit.bayes}{
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diff --git a/inst/doc/models/probit.bayes.tex b/inst/doc/models/probit.bayes.tex
new file mode 100644
index 0000000..7b49344
--- /dev/null
+++ b/inst/doc/models/probit.bayes.tex
@@ -0,0 +1,253 @@
+\section{\texttt{probit.bayes}: Bayesian Probit Regression}
+
+\label{probit.bayes}
+
+Use the probit regression model for model binary dependent variables
+specified as a function of a set of explanatory variables.  The model
+is estimated using a Gibbs sampler.  For other models suitable for
+binary response variables, see Bayesian logistic
+regression(\Sref{logit.bayes}), maximum likelihood logit regression
+(\Sref{logit}), and maximum likelihood probit regression
+(\Sref{probit}).  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Additional Inputs}
+
+Using the following arguments to monitor the Markov chains:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of 
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is 
+\texttt{NA} which corresponds to a random seed of 12345. 
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or vector with length equal to the number of
+estimated coefficients. The default is \texttt{NA}, such that the
+maximum likelihood estimates are used as the starting values.
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar value, that value will be the prior
+mean for all the coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar value, that value times an
+identity matrix will be the prior precision parameter. The default is
+0, which leads to an improper prior.
+\end{itemize}
+
+Use the following arguments to specify optional output for the model:
+\begin{itemize}
+\item \texttt{bayes.resid}: defaults to {\tt FALSE}.  If {\tt TRUE},
+the latent Bayesian residuals for all observations are returned.
+Alternatively, users can specify a vector of observations for which the
+latent residuals should be returned.
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCprobit)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(turnout)
+\end{verbatim}
+Estimating the probit regression using \texttt{probit.bayes}:
+\begin{verbatim}
+> z.out < - zelig(vote ~ race + educate, model = "probit.bayes",
+                  data = turnout, verbose = TRUE)
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim} %$
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given:
+\texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Estimating the first difference (and risk ratio) in individual's probability of
+voting  when education is set to be low (25th percentile) versus 
+high (75th percentile) while all the other variables are held at their 
+default values:
+\begin{verbatim}
+> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0.75))
+> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.25))
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+
+Let $Y_{i}$ be the binary dependent variable for observation $i$ which
+takes the value of either 0 or 1.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by
+\begin{eqnarray*}
+Y_{i}  &  \sim & \textrm{Bernoulli}(\pi_{i})\\
+&  = & \pi_{i}^{Y_{i}}(1-\pi_{i})^{1-Y_{i}},
+\end{eqnarray*}
+where $\pi_{i}=\Pr(Y_{i}=1)$.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\pi_{i}= \Phi(x_i \beta),
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the cumulative density function of the standard
+Normal distribution with mean 0 and variance 1, $x_{i}$ is the vector
+of $k$ explanatory variables for observation $i$, and $\beta$ is the
+vector of coefficients.
+
+\item The \emph{prior} for $\beta$ is given by
+\begin{eqnarray*}
+\beta \sim \textrm{Normal}_k \left(  b_{0}, B_{0}^{-1} \right)
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory variables
+and $B_{0}$ is the $k \times k$ precision matrix (the inverse of a
+variance-covariance matrix).
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the probit model are
+the predicted probability of a success:
+\begin{eqnarray*}
+E(Y \mid X) = \pi_{i}= \Phi(x_i \beta),
+\end{eqnarray*}
+given the posterior draws of $\beta$ from the MCMC iterations.
+
+\item The predicted values (\texttt{qi\$pr}) are draws from the Bernoulli
+distribution with mean equal to the simulated expected value $\pi_{i}$.
+
+\item The first difference (\texttt{qi\$fd}) for the probit model is defined
+as
+\begin{eqnarray*}
+\text{FD}=\Pr(Y=1\mid X_{1})-\Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item The risk ratio (\texttt{qi\$rr})is defined as
+\begin{eqnarray*}
+\text{RR}=\Pr(Y=1\mid X_{1})\ /\ \Pr(Y=1\mid X).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+
+\item In conditional prediction models, the average predicted treatment effect
+(\texttt{qi\$att.pr}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-\widehat{Y_{i}(t_{i}=0)}],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "probit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view 
+a default summary of information through \texttt{summary(z.out)}. 
+Other elements available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. 
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+
+\item \texttt{bayes.residuals}: When \texttt{bayes.residual} is \texttt{TRUE}
+or a set of observation numbers is given, this object contains the 
+posterior draws of the latent Bayesian residuals of all the observations 
+or the observations specified by the user. 
+
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected values (probabilities) for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$pr}: the simulated predicted values for the specified values
+of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values for the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$rr}: the simulated risk ratio for the expected values
+simulated from \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\item \texttt{qi\$att.pr}: the simulated average predicted treatment effect
+for the treated from conditional prediction models.
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+Bayesian probit regression \input{models/contributors2}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{probit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
diff --git a/inst/doc/models/probit.tex b/inst/doc/models/probit.tex
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+\section{{\tt probit}: Probit Regression for Dichotomous Dependent Variables}\label{probit}
+
+Use probit regression to model binary dependent variables
+specified as a function of a set of explanatory variables.  For a
+Bayesian implementation of this model, see \Sref{probit.bayes}.  
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "probit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out, x1 = NULL)
+\end{verbatim}
+
+\subsubsection{Additional Inputs} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for probit regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE} is
+selected, {\tt zelig()} computes robust standard errors via the {\tt
+sandwich} package (see \cite{Zeileis04}).  The default type of robust
+standard error is heteroskedastic and autocorrelation consistent (HAC),
+and assumes that observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame).  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Examples}
+Attach the sample turnout dataset:
+\begin{verbatim}
+> data(turnout)
+\end{verbatim}
+Estimate parameter values for the probit regression:
+\begin{verbatim}
+> z.out <- zelig(vote ~ race + educate,  model = "probit", data = turnout) 
+> summary(z.out)
+\end{verbatim}
+Set values for the explanatory variables to their default values.
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulate quantities of interest from the posterior distribution.
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.out)
+> summary(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+Let $Y_i$ be the observed binary dependent variable for observation
+$i$ which takes the value of either 0 or 1.
+\begin{itemize}
+\item The \emph{stochastic component} is given by  
+\begin{equation*}
+Y_i \; \sim \; \textrm{Bernoulli}(\pi_i), 
+\end{equation*}
+where $\pi_i=\Pr(Y_i=1)$.
+
+\item The \emph{systematic component} is 
+\begin{equation*}
+  \pi_i \; = \; \Phi (x_i \beta)
+\end{equation*}
+where $\Phi(\mu)$ is the cumulative distribution function of the
+Normal distribution with mean 0 and unit variance.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+
+\item The expected value ({\tt qi\$ev}) is a simulation of predicted
+  probability of success $$E(Y) = \pi_i = \Phi(x_i
+  \beta),$$ given a draw of $\beta$ from its sampling distribution.  
+
+\item The predicted value ({\tt qi\$pr}) is a draw from a Bernoulli
+  distribution with mean $\pi_i$.  
+  
+\item The first difference ({\tt qi\$fd}) in expected values is
+  defined as
+\begin{equation*}
+\textrm{FD} = \Pr(Y = 1 \mid x_1) - \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item The risk ratio ({\tt qi\$rr}) is defined as
+\begin{equation*}
+\textrm{RR} = \Pr(Y = 1 \mid x_1) / \Pr(Y = 1 \mid x).
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  Variation in
+    the simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "probit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: a vector of the in-sample fitted values.
+   \item {\tt linear.predictors}: a vector of $x_{i}\beta$.  
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt data}: the name of the input data frame.  
+   \end{itemize}
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values, or predicted
+     probabilities, for the specified values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from the
+     distributions defined by the predicted probabilities.  
+   \item {\tt qi\$fd}: the simulated first differences in the predicted
+     probabilities for the values specified in {\tt x} and {\tt x1}.
+   \item {\tt qi\$rr}: the simulated risk ratio for the predicted
+     probabilities simulated from {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The probit model is part of the base package by William N. Venables
+and Brian D. Ripley.  Please cite the model as:
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+In addition, advanced users may wish to refer to \texttt{help(glm)}
+and \texttt{help(family)}, as well as \cite{McCNel89}.
+
+Robust standard errors are implemented via the sandwich package
+by Achim Zeileis.  Please cite as
+\begin{verse}
+\bibentry{Zeileis04}.
+\end{verse}
+
+Sample data are a selection of $2,000$ observations from
+\begin{verse}
+\bibentry{KinTomWit00}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
diff --git a/inst/doc/models/relogit.aux b/inst/doc/models/relogit.aux
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@@ -0,0 +1,52 @@
+\relax 
+\citation{Zeileis04}
+\citation{Andrews91}
+\citation{LumHea99}
+\@writefile{toc}{\contentsline {section}{\numberline {12.30}{\tt  relogit}: Rare Events Logistic Regression for Dichotomous Dependent Variables}{275}{section.12.30}}
+\newlabel{relogit}{{12.30}{275}{{\tt relogit}: Rare Events Logistic Regression for Dichotomous Dependent Variables\relax }{section.12.30}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{275}{section*.348}}
+\@writefile{toc}{\contentsline {subsubsection}{Arguments}{275}{section*.349}}
+\citation{Zeileis04}
+\citation{KinZen01b}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{278}{section*.353}}
+\citation{KinZen02b}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{279}{section*.354}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{280}{section*.355}}
+\@writefile{toc}{\contentsline {subsubsection}{Differences with Stata Version}{281}{section*.356}}
+\citation{KinZen01b}
+\citation{KinZen01}
+\citation{KinZen02b}
+\citation{KinZen01b}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{282}{section*.357}}
+\@setckpt{models/relogit}{
+\setcounter{page}{283}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
+\setcounter{enumiv}{0}
+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{30}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{256}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
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+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/relogit.tex b/inst/doc/models/relogit.tex
new file mode 100644
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+\section{{\tt relogit}: Rare Events Logistic Regression for
+Dichotomous Dependent Variables}
+\label{relogit}
+
+The {\tt relogit} procedure estimates the same model as standard
+logistic regression (appropriate when you have a dichotomous dependent
+variable and a set of explanatory variables; see \Sref{logit}), but
+the estimates are corrected for the bias that occurs when the
+sample is small or the observed events are rare (i.e., if the
+dependent variable has many more 1s than 0s or the reverse).  The {\tt
+  relogit} procedure also optionally uses prior correction for
+case-control sampling designs.  
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, model = "relogit", tau = NULL,
+                 case.correct = c("prior", "weighting"), 
+                 bias.correct = TRUE, robust = FALSE, 
+                 data = mydata, ...)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Arguments}
+
+The {\tt relogit} procedure supports four optional arguments in
+addition to the standard arguments for {\tt zelig()}.  You may
+additionally use:  
+\begin{itemize}
+\item {\tt tau}: a vector containing either one or two values for
+  $\tau$, the true population fraction of ones.  Use, for example,
+  {\tt tau = c(0.05, 0.1)} to specify that the lower bound on {\tt
+  tau} is 0.05 and the upper bound is 0.1.  If left unspecified, only
+finite-sample bias correction is performed, not case-control correction.
+\item {\tt case.correct}: if {\tt tau} is specified, choose a method
+to correct for case-control sampling design: {\tt "prior"} (default)
+or {\tt "weighting"}.   
+\item {\tt bias.correct}: a logical value of {\tt TRUE} (default) or
+  {\tt FALSE} indicating whether the intercept should be corrected for
+  finite sample (rare events) bias.
+\item {\tt robust}:  defaults to {\tt FALSE} (except when {\tt
+case.control = "weighting"}; the default in this case becomes {\tt
+robust = TRUE}). If {\tt TRUE} is selected, {\tt zelig()} computes
+robust standard errors via the {\tt sandwich} package (see
+\cite{Zeileis04}).  The default type of robust standard error is
+heteroskedastic and autocorrelation consistent (HAC), and assumes that
+observations are ordered by time index.
+
+In addition, {\tt robust} may be a list with the following options:  
+\begin{itemize}
+\item {\tt method}:  Choose from 
+\begin{itemize}
+\item {\tt "vcovHAC"}: (default if {\tt robust = TRUE}) HAC standard
+errors. 
+\item {\tt "kernHAC"}: HAC standard errors using the
+weights given in \cite{Andrews91}. 
+\item {\tt "weave"}: HAC standard errors using the
+weights given in \cite{LumHea99}.  
+\end{itemize}  
+\item {\tt order.by}: defaults to {\tt NULL} (the observations are
+chronologically ordered as in the original data).  Optionally, you may
+specify a vector of weights (either as {\tt order.by = z}, where {\tt
+z} exists outside the data frame; or as {\tt order.by = \~{}z}, where
+{\tt z} is a variable in the data frame)  The observations are
+chronologically ordered by the size of {\tt z}.
+\item {\tt \dots}:  additional options passed to the functions 
+specified in {\tt method}.   See the {\tt sandwich} library and
+\cite{Zeileis04} for more options.   
+\end{itemize}
+\end{itemize}
+Note that if {\tt tau = NULL, bias.correct = FALSE, robust = FALSE},
+the {\tt relogit} procedure performs a standard logistic regression
+without any correction.
+
+\subsubsection*{Example 1: One Tau with Prior Correction and Bias Correction}
+
+Due to memory and space considerations, the data used here are a
+sample drawn from the full data set used in King and Zeng,
+2001,\nocite{KinZen01b}  The proportion of militarized interstate
+conflicts to the absence of disputes is $\tau = 1,042 / 303,772
+\approx 0.00343$.  To estimate the model,
+\begin{verbatim}
+> data(mid)
+> z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
+                  data = mid, model = "relogit", tau = 1042/303772)
+\end{verbatim}
+Summarize the model output:  
+\begin{verbatim}
+> summary(z.out1)
+\end{verbatim}
+Set the explanatory variables to their means:  
+\begin{verbatim}
+> x.out1 <- setx(z.out1)
+\end{verbatim}
+Simulate quantities of interest:
+\begin{verbatim}
+> s.out1 <- sim(z.out1, x = x.out1)
+> summary(s.out1)
+> plot(s.out1)
+\end{verbatim}
+
+\subsubsection*{Example 2: One Tau with Weighting, Robust Standard
+Errors, and Bias Correction} 
+
+Suppose that we wish to perform case control correction using
+weighting (rather than the default prior correction).  To
+estimate the model:  
+\begin{verbatim}
+> data(mid)
+> z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
+                  data = mid, model = "relogit", tau = 1042/303772, 
+                  case.control = "weighting", robust = TRUE)
+\end{verbatim}
+Summarize the model output:  
+\begin{verbatim}
+> summary(z.out2)
+\end{verbatim}
+Set the explanatory variables to their means:  
+\begin{verbatim}
+> x.out2 <- setx(z.out2)
+\end{verbatim}
+Simulate quantities of interest:
+\begin{verbatim}
+> s.out2 <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+\end{verbatim}
+
+\subsubsection*{Example 3: Two Taus with Bias Correction and Prior Correction}
+
+Suppose that we did not know that $\tau \approx 0.00343$, but only
+that it was somewhere between $(0.002, 0.005)$.  To estimate a model
+with a range of feasible estimates for $\tau$ (using the default prior
+correction method for case control correction):
+\begin{verbatim}
+> data(mid)
+> z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem 
+                  + years, data = mid, model = "relogit", 
+                  tau = c(0.002, 0.005))
+\end{verbatim}
+Summarize the model output:  
+\begin{verbatim}
+> summary(z.out2)
+\end{verbatim}
+Set the explanatory variables to their means:  
+\begin{verbatim}
+> x.out2 <- setx(z.out2)
+\end{verbatim}
+Simulate quantities of interest:
+\begin{verbatim}
+> s.out <- sim(z.out2, x = x.out2)
+> summary(s.out2)
+> plot(s.out2)
+\end{verbatim}
+The cost of giving a range of values for $\tau$ is that point
+estimates are not available for quantities of interest.  Instead,
+quantities are presented as confidence intervals with significance
+less than or equal to a specified level (e.g., at least 95\% of the
+simulations are contained in the nominal 95\% confidence interval).  
+
+\subsubsection{Model}
+
+\begin{itemize}
+\item Like the standard logistic regression, the \emph{stochastic
+    component} for the rare events logistic regression is:
+\begin{equation*}
+  Y_i \; \sim \; \textrm{Bernoulli}(\pi_i), 
+\end{equation*}
+where $Y_i$ is the binary dependent variable, and takes a value of
+either 0 or 1.
+
+\item The \emph{systematic component} is: 
+  \begin{equation*}
+    \pi_i \; = \; \frac{1}{1 + \exp(-x_i \beta)}.
+  \end{equation*}
+
+
+\item If the sample is generated via a case-control (or choice-based)
+  design, such as when drawing all events (or ``cases'') and a sample
+  from the non-events (or ``controls'') and going backwards to collect
+  the explanatory variables, you must correct for selecting on the
+  dependent variable.  While the slope coefficients are approximately
+  unbiased, the constant term may be significantly biased.  Zelig has
+two methods for case control correction:  
+\begin{enumerate}
+\item The ``prior correction'' method 
+adjusts the intercept term.  Let $\tau$ be the true population
+fraction of events, $\bar{y}$ the fraction of events in the sample,
+and $\hat{\beta_0}$ the uncorrected intercept term.  The corrected
+intercept $\beta_0$ is:
+\begin{equation*}
+\beta =  \hat{\beta_0} - \ln \left[ \bigg( \frac{1 - \tau}{\tau}
+  \bigg) \bigg( \frac{\bar{y}}{1 - \bar{y}} \bigg) \right].
+\end{equation*}
+
+\item The ``weighting'' method performs a weighted logistic regression to
+correct for a case-control sampling design.  Let the 1 subscript
+denote observations for which the dependent variable is observed as a
+1, and the 0 subscript denote observations for which the dependent
+variable is observed as a 0.  Then the vector of weights $w_i$
+\begin{eqnarray*}
+w_1 &=& \frac{\tau}{\bar{y}} \\
+w_0 &=& \frac{(1 - \tau)}{(1 - \bar{y})} \\
+w_i &=& w_1 Y_i + w_0 (1 - Y_i)
+\end{eqnarray*} 
+\end{enumerate}
+  If $\tau$ is unknown, you may alternatively specify an upper and
+  lower bound for the possible range of $\tau$.  In this case, the
+  {\tt relogit} procedure uses ``robust Bayesian'' methods to generate
+  a confidence interval (rather than a point estimate) for each
+  quantity of interest.  The nominal coverage of the confidence
+  interval is at least as great as the actual coverage.
+  
+\item By default, estimates of the the coefficients $\beta$ are
+  bias-corrected to account for finite sample or rare events bias.  In
+  addition, quantities of interest, such as predicted probabilities,
+  are also corrected of rare-events bias.  If $\widehat{\beta}$ are
+the uncorrected logit coefficients and bias($\widehat{\beta}$) is the
+bias term, the corrected coefficients $\tilde{\beta}$ are
+\begin{equation*}
+\widehat{\beta} - \textrm{bias}(\widehat{\beta}) = \tilde{\beta}
+\end{equation*}
+The bias term is
+\begin{equation*}
+\textrm{bias}(\widehat{\beta}) = (X'WX)^{-1} X'W \xi
+\end{equation*}
+where
+\begin{eqnarray*}
+\xi_i &=& 0.5 Q_{ii} \Big( (1 + w-1)\widehat{\pi}_i - w_1 \Big) \\
+Q &=& X(X'WX)^{-1} X' \\
+W = \textrm{diag}\{\widehat{\pi}_i (1 - \widehat{\pi}_i) w_i\}
+\end{eqnarray*}
+where $w_i$ and $w_1$ are given in the ``weighting'' section above.  
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item For either one or no $\tau$:  
+  \begin{itemize}
+  \item The expected values ({\tt qi\$ev}) for the rare events logit
+    are simulations of the predicted probability $$E(Y) = \pi_i =
+    \frac{1}{1 + \exp(-x_i \beta)},$$
+    given draws of $\beta$ from its posterior.
+  \item The predicted value ({\tt qi\$pr}) is a draw from a binomial
+    distribution with mean equal to the simulated $\pi_i$.  
+  \item The first difference ({\tt qi\$fd}) is defined as
+   \begin{equation*}
+   \textrm{FD} = \Pr(Y = 1 \mid x_1, \tau) - \Pr(Y = 1 \mid x, \tau).
+   \end{equation*}
+  \item The risk ratio ({\tt qi\$rr}) is defined as
+   \begin{equation*}
+   \textrm{RR} = \Pr(Y = 1 \mid x_1, \tau) \ / \ \Pr(Y = 1 \mid x, \tau).
+   \end{equation*}
+   \end{itemize}
+ \item For a range of $\tau$ defined by $[\tau_1, \tau_2]$, each of
+   the quantities of interest are $n \times 2$ matrices, which report
+   the lower and upper bounds, respectively, for a confidence interval
+   with nominal coverage at least as great as the actual coverage.  At
+   worst, these bounds are conservative estimates for the likely range
+   for each quantity of interest.  Please refer to \hlink{King and
+     Zeng
+     (2002)}{http://gking.harvard.edu/files/1s.pdf}\nocite{KinZen02b}
+   for the specific method of calculating bounded quantities of
+   interest.
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the treatment
+    ($t_i=1$) and control ($t_i=0$) groups.  Variation in the
+    simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$,
+    the counterfactual expected value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  Variation in
+    the simulations are due to uncertainty in simulating
+    $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of
+    $Y_i$ for observations in the treatment group, under the
+    assumption that everything stays the same except that the
+    treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "relogit", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may
+  extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt bias.correct}: {\tt TRUE} if bias correction was
+selected, else {\tt FALSE}.  
+    \item {\tt prior.correct}: {\tt TRUE} if prior correction was
+selected, else {\tt FALSE}. 
+    \item {\tt weighting}: {\tt TRUE} if weighting was selected, else
+{\tt FALSE}.  
+    \item {\tt tau}:  the value of {\tt tau} for which case control
+correction was implemented.  
+   \item {\tt residuals}: the working residuals in the final iteration
+     of the IWLS fit.
+   \item {\tt fitted.values}: the vector of fitted values for the
+     systemic component, $\pi_i$.
+   \item {\tt linear.predictors}: the vector of $x_{i} \beta$
+   \item {\tt aic}: Akaike's Information Criterion (minus twice the
+     maximized log-likelihood plus twice the number of coefficients).
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+   Note that for a range of $\tau$, each of the above items may be
+   extracted from the {\tt "lower.estimate"} and {\tt
+     "upper.estimate"} objects in your {\tt zelig} output.  Use {\tt
+     lower <- z.out\$lower.estimate}, and then {\tt
+     lower\$coefficients} to extract the coefficients for the
+   empirical estimate generated for the smaller of the two $\tau$.
+
+\item From {\tt summary(z.out)}, you may extract: 
+   \begin{itemize}
+   \item {\tt coefficients}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \item{\tt cov.scaled}: a $k \times k$ matrix of scaled covariances.
+   \item{\tt cov.unscaled}: a $k \times k$ matrix of unscaled
+     covariances.  
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values, or predicted
+     probabilities, for the specified values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from Binomial
+     distributions given the predicted probabilities.  
+   \item {\tt qi\$fd}: the simulated first difference in the predicted
+     probabilities for the values specified in {\tt x} and {\tt x1}.
+   \item {\tt qi\$rr}: the simulated risk ratio for the predicted
+     probabilities simulated from {\tt x} and {\tt x1}.
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Differences with Stata Version}
+The Stata version of ReLogit and the R implementation differ slightly
+in their coefficient estimates due to differences in the matrix
+inversion routines implemented in R and Stata.  Zelig uses
+orthogonal-triangular decomposition (through {\tt lm.influence()}) to
+compute the bias term, which is more numerically stable than
+standard matrix calculations.
+
+\subsubsection{Contributors}
+
+Please cite the rare events logit model as:
+\begin{verse}
+\bibentry{KinZen01b}.
+\end{verse}
+\begin{verse}
+\bibentry{KinZen01}.
+\end{verse} 
+\begin{verse}
+\bibentry{KinZen02b}.
+\end{verse}
+
+Sample data are a selection of observations from 
+\begin{verse}
+\bibentry{KinZen01b}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
diff --git a/inst/doc/models/sur.tex b/inst/doc/models/sur.tex
new file mode 100644
index 0000000..846a8be
--- /dev/null
+++ b/inst/doc/models/sur.tex
@@ -0,0 +1,188 @@
+\documentclass[12pt]{book}%
+\usepackage{amsfonts}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{graphicx}
+\usepackage{fullpage}
+\usepackage{multirow}
+\usepackage{hyperref}%
+\usepackage{bibentry}
+\begin{document}
+\subsection{\texttt{sur}: Seemingly Unrelated Regression}
+\label{sur}
+\texttt{sur} extends ordinary least squares analysis to estimate system of linear 
+equations with correlated error terms. The seemingly unrelated regression model can
+be viewed as a special case of generalized least squares.
+\subsubsection{Syntax}
+\begin{verbatim}
+> fml <- list ("mu1" = Y1 ~ X1,
+	       "mu2" = Y2 ~ X2,
+               "mu3" = Y3 ~ X3)
+> z.out<-zelig(formula = fml, model = "2sls", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+\subsubsection{Inputs}
+\texttt{sur} regression specification has at least $M$ equations 
+($M \ge 2$) corresponding to the dependent variables ($Y_1, Y_2, \ldots, Y_M$).
+\begin{itemize}
+\item \texttt{formula}:a list whose elements are formulae corresponding to 
+the $M$ equations and their respective dependent and explanatory variables.
+For example, when there are no constraints on the coefficients:
+\begin{verbatim}
+> fml <- list ("mu1" = Y1 ~ X1,
+	       "mu2" = Y2 ~ X2,
+               "mu3" = Y3 ~ X3)
+\end{verbatim}
+\texttt{"mu1"} is the label for the first equation with Y1 as the dependent variable
+and X1 as the explanatory variable. Similarly \texttt{"mu2"} and \texttt{"mu3"} are the
+labels for the Y2 and Y3 equations.
+\item \texttt{tag}: Users can also put constraints on the coefficients by using
+the special function \texttt{tag}. \texttt{tag} takes two parameters. The first
+parameter is the variable whose coefficient needs to be constrained and the second
+parameter is label for the constrained coefficient. Each label uniquely identifies
+the constrained coefficient. For example:
+\begin{verbatim}
+> fml <- list ("mu1" = Y1 ~ tag(Xc,"constrain1")+ X1,
+	       "mu2" = Y2 ~ tag(Xc,"constrain1")+ X2,
+               "mu3" = Y3 ~ X3)
+\end{verbatim}
+\end{itemize}
+\subsubsection{Additional Inputs}
+\texttt{sur} takes the following additional inputs for model
+specifications:
+\begin{itemize}
+\item \texttt{TX}: an optional matrix to transform the regressor
+matrix and, hence, also the coefficient vector (see details). Default is \texttt{NULL}.
+\item \texttt{maxiter}: maximum number of iterations.
+\item \texttt{tol}: tolerance level indicating when to stop the iteration.
+\item \texttt{rcovformula}: formula to calculate the estimated residual covariance
+matrix (see details). Default is equal to 1.
+\item \texttt{probdfsys}: use the degrees of freedom of the whole system
+(in place of the degrees of freedom of the single equation to calculate probability
+values for the t-test of individual parameters. 
+\item \texttt{solvetol}: tolerance level for detecting linear dependencies when 
+inverting a matrix or calculating a determinant. Default is \texttt {solvetol}= \begin{verbatim}.Machine\$double.eps.\end{verbatim}
+\item \texttt{saveMemory}: logical. Save memory by omitting some calculation that are
+not crucial for the basic estimate (e.g McElroy's $R^2$).
+\end{itemize}
+\subsubsection{Details}
+The matrix \texttt{TX} transforms the regressor matrix ($X$) by $X\ast=X \times TX$. Thus,
+the vector of coefficients is now $b=TX \times b\ast$ where $b$ is the original(stacked) 
+vector of all coefficients and $b\ast$ is the new coefficient vector that is estimated instead.
+Thus, the elements of vector $b$ and $b_i = \sum_j TX_{ij}\times b_j\ast$. The $TX$ matrix can be
+used to change the order of the coefficients and also to restrict coefficients (if $TX$ has 
+less columns than it has rows). 
+If iterated (with \texttt{maxit}>1), the covergence criterion is
+\begin{eqnarray*}
+\sqrt{\frac{\sum_i(b_{i,g}-b_{i,g-1})^2}{\sum_ib_{i,g-1}^2}}< tol
+\end{eqnarray*}
+where $b_{i,g}$ is the ith coefficient of the gth iteration step.
+The formula (\texttt{rcovformula} to calculate the estimated covariance matrix of the residuals($\hat{\Sigma}$)can be one
+of the following (see Judge et al., 1955, p.469):
+if \texttt{rcovformula}= 0:
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T}
+\end{eqnarray*}
+if \texttt{rcovformula}= 1 or \texttt{rcovformula}='geomean':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{\sqrt{(T-k_i)\times (T-k_j)}}
+\end{eqnarray*}
+if \texttt{rcovformula}= 2 or \texttt{rcovformula}='Theil':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-k_i-k_j+tr[X_i(X_i\prime X_i)^{-1}X_i\prime X_j(X_j\prime X_j)^{-1}X_j\prime]}
+\end{eqnarray*}
+if \texttt{rcovformula}= 3 or \texttt{rcovformula}='max':
+\begin{eqnarray*}
+\hat{\sigma_{ij}}= \frac{\hat{e_i}\prime\hat{e_j}}{T-max(k_i,k_j)}
+\end{eqnarray*}
+If $i = j$, formula 1, 2, and 3 are equal. All these three formulas yield unbiased estimators
+for the diagonal elements of the residual covariance matrix. If $i neq j$, only formula 2
+yields an unbiased estimator for the residual covariance matrix, but it is not necessarily
+positive semidefinit. Thus, it is doubtful whether formula 2 is really superior to formula 1
+(Theil, 1971, p.322).
+\subsubsection{Examples}
+ Attaching the example dataset:
+ \begin{verbatim} 
+ > data(grunfeld)
+ \end{verbatim}
+ Formula:
+\begin{verbatim}
+ >formula<-list(mu1=Ige~Fge+Cge,
+              mu2=Iw~Fw+Cw)
+\end{verbatim}
+Estimating the model using \texttt{sur}:
+\begin{verbatim}
+> z.out<-zelig(formula,"sur",data=grunfeld)
+> summary(z.out)
+\end{verbatim}
+
+Set explanatory variables to their default (mean/mode) values
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+
+Simulate draws from the posterior distribution:
+\begin{verbatim}
+> s.out <- sim(z.out)
+> summary(s.out)
+\end{verbatim}
+\clearpage
+\subsubsection{Model}
+The basic seemingly unrelated regression model assumes that for each individual
+observation $i$ there are $M$ dependent variables ($Y_{ij}, j=1,\ldots,M$) 
+each with its own regression equation:
+\begin{eqnarray*}
+Y_{ij} = X_{ij}'\beta_j + \epsilon_{ij}, \quad  {\rm for} \quad  i=1,\ldots,N \quad {\rm and} \quad  j=1,\ldots,M
+\end{eqnarray*}
+when $X_{ij}$ is a k-vector of explanatory variables, $\beta_j$
+is the coefficients of the explanatory variables,
+\begin{itemize}
+\item The \emph{stochastic component} is:
+\begin{eqnarray*}
+\epsilon_{ij}  &  \sim & {\cal N}(0, \sigma_{ij})
+\end{eqnarray*}
+where within each $j$ equation, $epsilon_{ij}$ is identically
+and independently distributed for $i=1,\ldots,M$,
+\begin{eqnarray*}
+{\rm Var}(\epsilon_{ij})=\sigma_j \quad {\rm and} \quad {\rm Cov}(\epsilon_{ij}, \epsilon_{i\prime j})= 0, \quad {\rm for} \quad i \neq i\prime,\quad {\rm and} \quad j=1,\ldots,M 
+\end{eqnarray*}
+However, the error terms for the \emph{ith} observation can be correlated across equations
+\begin{eqnarray*}
+{\rm Cov}(\epsilon_{ij}, \epsilon_{ij\prime})\neq 0, \quad {\rm for} \quad j \neg j\prime, \quad {\rm and} \quad i=1,\ldots,N 
+\end{eqnarray*}
+\item The \emph{systematic component} is:
+\begin{eqnarray*}
+\mu_{ij}= E(Y_ij)= X_{ij}\beta_j, \quad {\rm for} \quad  i=1,\ldots,N, \quad {\rm and} \quad j=1,\ldots,M 
+\end{eqnarray*}
+\end{itemize}
+\subsubsection{See Also}
+For information about two stage least squares regression, see 
+\Sref{2sls} and \texttt{help(2sls)}.
+For information about three stage least squares regression, see
+\Sref{3sls} and \texttt{help(3sls)}.
+\subsubsection{Quantities of Interest}
+\subsubsection{Output Values}
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(formula=fml, model = "sur", data)
+\end{verbatim}
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a default
+summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below:
+\begin{itemize}
+\item \texttt{rcovest}: residual covariance matrix used for estimation.
+\item \texttt{mcelr2}: McElroys R-squared value for the system.
+\end{itemize}
+\input{systemfit_output}
+\begin{itemize}
+\item \texttt{maxiter}: maximum number of iterations.
+\item \texttt{tol}: tolerance level indicating when to stop the iteration.
+\end{itemize}
+\subsubsection{Contributors}
+The \texttt{sur} function is adapted from \input{contributors_systemfit}
+\end{document}
+
diff --git a/inst/doc/models/systemfit_output.tex b/inst/doc/models/systemfit_output.tex
new file mode 100644
index 0000000..a338b02
--- /dev/null
+++ b/inst/doc/models/systemfit_output.tex
@@ -0,0 +1,55 @@
+\begin{itemize}
+\item \texttt{method}: Estimation method. 
+\item \texttt{g}: number of equations.
+\item \texttt{n}: total number of observations.
+\item \texttt{k}: total number of coefficients.
+\item \texttt{ki}: total number of linear independent coefficients.
+\item \texttt{df}: degrees of freedom of the whole system.
+\item \texttt{iter}: number of iteration steps.
+\item \texttt{b}: vector of all estimated coefficients.
+\item \texttt{t}: $t$ values for $b$.
+\item \texttt{se}: estimated standard errors of $b$.
+\item \texttt{bt}: coefficient vector transformed by $TX$.
+\item \texttt{p}: $p$ values for $b$.
+\item \texttt{bcov}: estimated covariance matrix of $b$.
+\item \texttt{btcov}: covariance matrix of $bt$.
+\item \texttt{rcov}: estimated residual covariance matrix.
+\item \texttt{drcov}: determinant of \texttt{rcov}.
+\item \texttt{rcor}: estimated residual correlation matrix.
+\item \texttt{olsr2}: system OLS R-squared value.
+\item \texttt{y}: vector of all (stacked) endogenous variables.
+\item \texttt{x}: matrix of all (diagonally stacked) regressors.
+\item \texttt{data}: data frame of the whole system (including instruments).
+\item \texttt{TX}: matrix used to transform the regressor matrix.
+\item \texttt{rcovformula}: formula to calculate the estimated residual covariance matrix.
+\item \texttt{probdfsys}: system degrees of freedom to calculate probability values?.
+\item \texttt{solvetol}: tolerance level when inverting a matrix or calculating a determinant.
+\item \texttt{eq}: a list that contains the results that belong to the individual equations.
+\item \texttt{eqnlabel*}: the equation label of the ith equation (from the labels list).
+\item \texttt{formula*}: model formula of the ith equation.
+\item \texttt{n*}: number of observations of the ith equation.
+\item \texttt{k*}: number of coefficients/regressors in the ith equation (including the constant).
+\item \texttt{ki*}: number of linear independent coefficients in the ith equation (including the
+constant differs from k only if there are restrictions that are not cross equation).
+\item \texttt{df*}: degrees of freedom of the ith equation.
+\item \texttt{b*}: estimated coefficients of the ith equation. 
+\item \texttt{se*}: estimated standard errors of $b$ of the ith equation.
+\item \texttt{t*}: $t$ values for $b$ of the ith equation.
+\item \texttt{p*}: $p$ values for $b$ of the ith equation.
+\item \texttt{covb*}: estimated covariance matrix of $b$ of the ith equation.
+\item \texttt{y*}: vector of endogenous variable (response values) of the ith equation.
+\item \texttt{x*}: matrix of regressors (model matrix) of the ith equation.
+\item \texttt{data*}: data frame (including instruments) of the ith equation.
+\item \texttt{fitted*}: vector of fitted values of the ith equation.
+\item \texttt{residuals*}: vector of residuals of the ith equaiton.
+\item \texttt{ssr*}: sum of squared residuals of the ith equation.
+\item \texttt{mse*}: estimated variance of the residuals (mean of squared errors) of the ith equation.
+\item \texttt{s2*}: estimated variance of the residents($\hat{sigma}^2$) of the ith equation.
+\item \texttt{rmse*}: estimated standard error of the reiduals (square root of mse) of the ith equation.
+\item \texttt{s*}: estimated standard error of the residuals ($\hat{\sigma})$ of the ith equation.
+\item \texttt{r2*}: R-squared (coefficient of determination).
+\item \texttt{adjr2*}: adjusted R-squared value.
+
+
+
+\end{itemize}
diff --git a/inst/doc/models/tobit.aux b/inst/doc/models/tobit.aux
new file mode 100644
index 0000000..b38957d
--- /dev/null
+++ b/inst/doc/models/tobit.aux
@@ -0,0 +1,46 @@
+\relax 
+\citation{Huber81}
+\citation{White80}
+\@writefile{toc}{\contentsline {section}{\numberline {12.31}\texttt  {tobit}: Linear Regression for a Left-Censored Dependent Variable}{283}{section.12.31}}
+\newlabel{tobit}{{12.31}{283}{\texttt {tobit}: Linear Regression for a Left-Censored Dependent Variable\relax }{section.12.31}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{283}{section*.358}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{283}{section*.359}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{284}{section*.360}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{284}{section*.361}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{285}{section*.362}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{285}{section*.363}}
+\citation{VenRip02}
+\citation{KinAltBur90}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{286}{section*.364}}
+\@setckpt{models/tobit}{
+\setcounter{page}{287}
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diff --git a/inst/doc/models/tobit.bayes.aux b/inst/doc/models/tobit.bayes.aux
new file mode 100644
index 0000000..1f1c346
--- /dev/null
+++ b/inst/doc/models/tobit.bayes.aux
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+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {12.32}\texttt  {tobit.bayes}: Bayesian Linear Regression for a Censored Dependent Variable}{287}{section.12.32}}
+\newlabel{tobit.bayes}{{12.32}{287}{\texttt {tobit.bayes}: Bayesian Linear Regression for a Censored Dependent Variable\relax }{section.12.32}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{287}{section*.365}}
+\@writefile{toc}{\contentsline {subsubsection}{Inputs}{287}{section*.366}}
+\@writefile{toc}{\contentsline {subsubsection}{Additional Inputs}{287}{section*.367}}
+\@writefile{toc}{\contentsline {subsubsection}{Convergence}{288}{section*.368}}
+\@writefile{toc}{\contentsline {subsubsection}{Examples}{289}{section*.369}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{290}{section*.370}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{290}{section*.371}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{291}{section*.372}}
+\citation{MarQui05}
+\citation{PluBesCowVin05}
+\citation{MarQui05}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{292}{section*.373}}
+\@setckpt{models/tobit.bayes}{
+\setcounter{page}{293}
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diff --git a/inst/doc/models/tobit.bayes.tex b/inst/doc/models/tobit.bayes.tex
new file mode 100644
index 0000000..282c722
--- /dev/null
+++ b/inst/doc/models/tobit.bayes.tex
@@ -0,0 +1,262 @@
+\section{\texttt{tobit.bayes}: Bayesian Linear Regression for a
+Censored Dependent Variable} \label{tobit.bayes}
+
+Bayesian tobit regression estimates a linear regression model with a
+censored dependent variable using a Gibbs sampler.  The dependent
+variable may be censored from below and/or from above.  For other
+linear regression models with fully observed dependent variables, see
+Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
+regression (\Sref{normal}), or least squares (\Sref{ls}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf, 
+                  model = "tobit.bayes", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments to specify how the
+dependent variable is censored.
+\begin{itemize}
+\item \texttt{below}: point at which the dependent variable is censored
+from below. If the dependent variable is only censored from above, set 
+\texttt{below = -Inf}. The default value is 0.
+\item \texttt{above}: point at which the dependent variable is censored
+from above. If the dependent variable is only censored from below, set 
+\texttt{above = Inf}. The default value is \texttt{Inf}.
+\end{itemize}
+
+\subsubsection{Additional Inputs}
+
+Use the following arguments to monitor the convergence of the Markov
+chain:  
+\begin{itemize}
+\item \texttt{burnin}: number of the initial MCMC iterations to be 
+ discarded (defaults to 1,000).
+
+\item \texttt{mcmc}: number of the MCMC iterations after burnin
+(defaults to 10,000).
+
+\item \texttt{thin}: thinning interval for the Markov chain. Only every 
+ \texttt{thin}-th draw from the Markov chain is kept. The value of
+\texttt{mcmc} must be divisible by this value. The default value is 1.
+
+\item \texttt{verbose}: defaults to {\tt FALSE}.  If \texttt{TRUE},
+the progress of the sampler (every $10\%$) is printed to the screen.
+
+\item \texttt{seed}: seed for the random number generator. The default is 
+\texttt{NA} which corresponds to a random seed of 12345. 
+
+\item \texttt{beta.start}: starting values for the Markov 
+chain, either a scalar or vector with length equal to the number 
+of estimated coefficients. The default is \texttt{NA}, such that the
+least squares estimates are used as the starting values.  
+
+\end{itemize}
+
+Use the following parameters to specify the model's priors:  
+\begin{itemize}
+\item \texttt{b0}: prior mean for the coefficients, either a numeric
+vector or a scalar. If a scalar, that value will be the prior mean for
+all coefficients. The default is 0.
+
+\item \texttt{B0}: prior precision parameter for the coefficients,
+either a square matrix (with the dimensions equal to the number of the
+coefficients) or a scalar. If a scalar, that value times an identity
+matrix will be the prior precision parameter. The default is 0, which
+leads to an improper prior.
+
+\item \texttt{c0}: \texttt{c0/2} is the shape parameter for the Inverse Gamma
+prior on the variance of the disturbance terms. 
+
+\item \texttt{d0}: \texttt{d0/2} is the scale parameter for the Inverse Gamma
+prior on the variance of the disturbance terms. 
+
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(MCMCtobit)} for more 
+information.
+
+\input{models/coda_diag}
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(tobin)
+\end{verbatim}
+Estimating linear regression using \texttt{tobit.bayes}:
+\begin{verbatim}
+> z.out < - zelig(durable ~ age + quant, model = "tobit.bayes",
+                  data = tobin, verbose=TRUE)
+\end{verbatim}
+Checking for convergence before summarizing the estimates:
+\begin{verbatim}
+> geweke.diag(z.out$coefficients)
+> heidel.diag(z.out$coefficients)
+> raftery.diag(z.out$coefficients)
+> summary(z.out)
+\end{verbatim} %$
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given 
+\texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
+
+\begin{verbatim}
+> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
+> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
+\end{verbatim}
+Estimating the first difference for the effect of
+high versus low liquidity ratio on duration(\texttt{durable}):
+\begin{verbatim}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection{Model}
+Let $Y_i^*$ be the dependent variable which is not directly observed. Instead,
+we observe $Y_i$ which is defined as following:
+\begin{equation*}
+Y_i = \left\{
+\begin{array}{lcl}
+Y_i^*  &\textrm{if} & c_1<Y_i^*<c_2 \\
+c_1    &\textrm{if} & c_1 \ge Y_i^* \\
+c_2    &\textrm{if} & c_2 \le Y_i^*
+\end{array}\right.
+\end{equation*}
+where $c_1$ is the lower bound below which $Y_i^*$ is censored, and
+$c_2$ is the upper bound above which $Y_i^*$ is censored.
+
+\begin{itemize}
+\item The \emph{stochastic component} is given by 
+\begin{eqnarray*} 
+\epsilon_{i}  &  \sim & \textrm{Normal}(0, \sigma^2)
+\end{eqnarray*} 
+where $\epsilon_{i}=Y^*_i-\mu_i$. 
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i}= x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+
+\item The \emph{semi-conjugate priors} for $\beta$ and $\sigma^2$ are
+given by 
+\begin{eqnarray*} 
+\beta & \sim & \textrm{Normal}_k \left( b_{0},B_{0}^{-1}\right) \\
+\sigma^{2} & \sim & \textrm{InverseGamma} \left( \frac{c_0}{2}, \frac{d_0}{2}
+\right) 
+\end{eqnarray*}
+where $b_{0}$ is the vector of means for the $k$ explanatory
+variables, $B_{0}$ is the $k\times k$ precision matrix (the inverse of
+a variance-covariance matrix), and $c_0/2$ and $d_0/2$ are the shape
+and scale parameters for $\sigma^{2}$.  Note that $\beta$ and
+$\sigma^2$ are assumed \emph{a priori} independent.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the tobit regression model is
+calculated as following.  Let
+\begin{eqnarray*}
+\Phi_1 &=& \Phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
+\Phi_2 &=& \Phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) \\ 
+\phi_1 &=& \phi\left(\frac{(c_1 - x \beta)}{\sigma}\right) \\
+\phi_2 &=& \phi\left(\frac{(c_2 - x \beta)}{\sigma}\right) 
+\end{eqnarray*}
+where $\Phi(\cdot)$ is the (cumulative) Normal density function and
+$\phi(\cdot)$ is the Normal probability density function of the
+standard normal distribution.  Then the expected values are
+\begin{eqnarray*}
+E(Y|x) &=& P(Y^* \le c_1|x) c_1+P(c_1<Y^*<c_2|x) E(Y^* \mid c_1<Y^*<c_2, x)+P(Y^* \ge c_2) c_2 \\
+   &=& \Phi_{1}c_1 + x \beta(\Phi_{2}-\Phi_{1}) + \sigma (\phi_1 -\phi_2) + (1-\Phi_2) c_2,
+\end{eqnarray*}
+
+\item The first difference (\texttt{qi\$fd}) for the tobit regression
+model is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y\mid x_{1})-E(Y\mid x).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[Y_{i}(t_{i}=1)-E[Y_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "tobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a 
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws 
+of the variance $\sigma^2$.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected value for the specified
+values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+values given the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment effect
+for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+Bayesian tobit regression \input{models/contributors}
+
+\noindent Ben Goodrich and Ying Lu enabled \texttt{tobit.bayes} to work with Zelig.
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+
+
+%\end{document}
diff --git a/inst/doc/models/tobit.tex b/inst/doc/models/tobit.tex
new file mode 100644
index 0000000..6893553
--- /dev/null
+++ b/inst/doc/models/tobit.tex
@@ -0,0 +1,208 @@
+\section{\texttt{tobit}: Linear Regression for a
+Left-Censored Dependent Variable} \label{tobit}
+
+Tobit regression estimates a linear regression model for a
+left-censored dependent variable, where the dependent variable is
+censored from below.  While the classical tobit model has values
+censored at 0, you may select another censoring point.  For other
+linear regression models with fully observed dependent variables, see
+Bayesian regression (\Sref{normal.bayes}), maximum likelihood normal
+regression (\Sref{normal}), or least squares (\Sref{ls}).
+
+\subsubsection{Syntax}
+\begin{verbatim}
+> z.out <- zelig(Y ~ X1 + X2, below = 0, above = Inf, 
+                  model = "tobit", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+
+\subsubsection{Inputs}
+{\tt zelig()} accepts the following arguments to specify how the
+dependent variable is censored.
+\begin{itemize}
+\item \texttt{below}: (defaults to 0)  The point at which the dependent
+variable is censored from below.  If any values in the dependent
+variable are observed to be less than the censoring point, it is
+assumed that that particular observation is censored from below at the
+observed value.  (See \Sref{tobit.bayes} for a Bayesian
+implementation that supports both left and right censoring.) 
+ \item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) and the options selected in
+{\tt cluster}.
+\item {\tt cluster}:  if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations.  Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata.  Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3", 
+                 model = "tobit", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters.  If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.  
+\end{itemize}
+
+Zelig users may wish to refer to \texttt{help(survreg)} for more 
+information.
+
+\subsubsection{Examples}
+
+\begin{enumerate}
+\item {Basic Example} \\
+Attaching the sample  dataset:
+\begin{verbatim}
+> data(tobin)
+\end{verbatim}
+Estimating linear regression using \texttt{tobit}:
+\begin{verbatim}
+> z.out < - zelig(durable ~ age + quant, model = "tobit", data = tobin)
+\end{verbatim}
+Setting values for the explanatory variables to their sample averages:
+\begin{verbatim}
+> x.out <- setx(z.out)
+\end{verbatim}
+Simulating quantities of interest from the posterior distribution given 
+\texttt{x.out}.
+\begin{verbatim}
+> s.out1 <- sim(z.out, x = x.out)
+> summary(s.out1)
+\end{verbatim}
+\item {Simulating First Differences} \\
+Set explanatory variables to their default(mean/mode) values, with high
+(80th percentile) and low (20th percentile) liquidity ratio (\texttt{quant}):
+
+\begin{verbatim}
+> x.high <- setx(z.out, quant = quantile(tobin$quant, prob = 0.8))
+> x.low <- setx(z.out, quant = quantile(tobin$quant, prob = 0.2))
+\end{verbatim}
+Estimating the first difference for the effect of
+high versus low liquidity ratio on duration(\texttt{durable}):
+\begin{verbatim}
+> s.out2 <- sim(z.out, x = x.high, x1 = x.low)
+> summary(s.out2)
+\end{verbatim}
+\end{enumerate} 
+
+\subsubsection{Model}
+\begin{itemize} 
+\item Let $Y_i^*$ be a latent dependent variable which is distributed with
+\emph{stochastic} component
+\begin{eqnarray*}
+Y_i^* & \sim & \textrm{Normal}(\mu_i, \sigma^2) \\
+\end{eqnarray*}
+where $\mu_i$ is a vector means and $\sigma^2$ is a scalar variance
+parameter.  $Y_i^*$ is not directly observed, however.  Rather we
+observed $Y_i$ which is defined as:  
+\begin{equation*}
+Y_i = \left\{
+\begin{array}{lcl}
+Y_i^*  &\textrm{if} & c <Y_i^* \\
+c    &\textrm{if} & c \ge Y_i^* 
+\end{array}\right.
+\end{equation*}
+where $c$ is the lower bound below which $Y_i^*$ is censored.
+
+\item The \emph{systematic component} is given by
+\begin{eqnarray*}
+\mu_{i} &=& x_{i} \beta,
+\end{eqnarray*}
+where $x_{i}$ is the vector of $k$ explanatory variables for
+observation $i$ and $\beta$ is the vector of coefficients.
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values (\texttt{qi\$ev}) for the tobit regression
+model are the same as the expected value of $Y*$:  
+\begin{equation*}
+E(Y^* | X) = \mu_{i} = x_{i} \beta
+\end{equation*}
+
+\item The first difference (\texttt{qi\$fd}) for the tobit regression
+model is defined as
+\begin{eqnarray*}
+\text{FD}=E(Y^* \mid x_{1}) - E(Y^* \mid x).
+\end{eqnarray*}
+
+\item In conditional prediction models, the average expected treatment effect
+(\texttt{qi\$att.ev}) for the treatment group is
+\begin{eqnarray*}
+\frac{1}{\sum t_{i}}\sum_{i:t_{i}=1}[E[Y^*_{i}(t_{i}=1)]-E[Y^*_{i}(t_{i}=0)]],
+\end{eqnarray*}
+where $t_{i}$ is a binary explanatory variable defining the treatment
+($t_{i}=1$) and control ($t_{i}=0$) groups. 
+
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you may
+view. For example, if you run:
+\begin{verbatim}
+z.out <- zelig(y ~ x, model = "tobit.bayes", data)
+\end{verbatim}
+
+\noindent then you may examine the available information in \texttt{z.out} by
+using \texttt{names(z.out)}, see the draws from the posterior distribution of
+the \texttt{coefficients} by using \texttt{z.out\$coefficients}, and view a 
+default summary of information through \texttt{summary(z.out)}. Other elements
+available through the \texttt{\$} operator are listed below.
+
+\begin{itemize}
+\item From the \texttt{zelig()} output object \texttt{z.out}, you may extract:
+
+\begin{itemize}
+\item \texttt{coefficients}: draws from the posterior distributions
+of the estimated parameters. The first $k$ columns contain the posterior draws
+of the coefficients $\beta$, and the last column contains the posterior draws 
+of the variance $\sigma^2$.
+
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+\item \texttt{seed}: the random seed used in the model.
+
+\end{itemize}
+
+\item From the \texttt{sim()} output object \texttt{s.out}:
+
+\begin{itemize}
+\item \texttt{qi\$ev}: the simulated expected value for the specified
+  values of \texttt{x}.
+
+\item \texttt{qi\$fd}: the simulated first difference in the expected
+  values given the values specified in \texttt{x} and \texttt{x1}.
+
+\item \texttt{qi\$att.ev}: the simulated average expected treatment
+  effect for the treated from conditional prediction models.
+
+\end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The tobit function is part of the survival library by Terry
+Therneau, ported to R by Thomas Lumley.  Advanced users may wish to
+refer to \texttt{help(survfit)} in the survival library and
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinAltBur90}.
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality. 
+
+%%% Local Variables:
+%%% mode: latex
+%%% TeX-master: t
+%%% End:
+
+
+
+%\end{document}
diff --git a/inst/doc/models/weibull.aux b/inst/doc/models/weibull.aux
new file mode 100644
index 0000000..a704e8d
--- /dev/null
+++ b/inst/doc/models/weibull.aux
@@ -0,0 +1,46 @@
+\relax 
+\citation{Huber81}
+\citation{White80}
+\@writefile{toc}{\contentsline {section}{\numberline {12.33}{\tt  weibull}: Weibull Regression for Duration Dependent Variables}{293}{section.12.33}}
+\newlabel{weibull}{{12.33}{293}{{\tt weibull}: Weibull Regression for Duration Dependent Variables\relax }{section.12.33}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Syntax}{293}{section*.374}}
+\@writefile{toc}{\contentsline {subsubsection}{Input Values}{293}{section*.375}}
+\@writefile{toc}{\contentsline {subsubsection}{Example}{294}{section*.376}}
+\@writefile{toc}{\contentsline {subsubsection}{Model}{294}{section*.377}}
+\@writefile{toc}{\contentsline {subsubsection}{Quantities of Interest}{295}{section*.378}}
+\@writefile{toc}{\contentsline {subsubsection}{Output Values}{296}{section*.379}}
+\citation{VenRip02}
+\citation{KinAltBur90}
+\@writefile{toc}{\contentsline {subsubsection}{Contributors}{297}{section*.380}}
+\@setckpt{models/weibull}{
+\setcounter{page}{298}
+\setcounter{equation}{1}
+\setcounter{enumi}{2}
+\setcounter{enumii}{3}
+\setcounter{enumiii}{2}
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+\setcounter{footnote}{0}
+\setcounter{mpfootnote}{0}
+\setcounter{part}{3}
+\setcounter{chapter}{12}
+\setcounter{section}{33}
+\setcounter{subsection}{0}
+\setcounter{subsubsection}{0}
+\setcounter{paragraph}{0}
+\setcounter{subparagraph}{0}
+\setcounter{figure}{0}
+\setcounter{table}{0}
+\setcounter{NAT at ctr}{0}
+\setcounter{parentequation}{0}
+\setcounter{Item}{260}
+\setcounter{Hfootnote}{10}
+\setcounter{lpart}{0}
+\setcounter{lchapter}{0}
+\setcounter{lsection}{0}
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+\setcounter{lsubsubsection}{0}
+\setcounter{lparagraph}{0}
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+\setcounter{section at level}{3}
+}
diff --git a/inst/doc/models/weibull.tex b/inst/doc/models/weibull.tex
new file mode 100644
index 0000000..7b27063
--- /dev/null
+++ b/inst/doc/models/weibull.tex
@@ -0,0 +1,267 @@
+\section{{\tt weibull}: Weibull Regression for Duration
+Dependent Variables}\label{weibull}
+
+Choose the Weibull regression model if the values in your dependent
+variable are duration observations.  The Weibull model relaxes the
+exponential model's (see \Sref{exp}) assumption of constant hazard,
+and allows the hazard rate to increase or decrease monotonically with
+respect to elapsed time.
+
+\subsubsection{Syntax}
+
+\begin{verbatim}
+> z.out <- zelig(Surv(Y, C) ~ X1 + X2, model = "weibull", data = mydata)
+> x.out <- setx(z.out)
+> s.out <- sim(z.out, x = x.out)
+\end{verbatim}
+Weibull models require that the dependent variable be in the form {\tt
+  Surv(Y, C)}, where {\tt Y} and {\tt C} are vectors of length $n$.
+For each observation $i$ in 1, \dots, $n$, the value $y_i$ is the
+duration (lifetime, for example), and the associated $c_i$ is a binary
+variable such that $c_i = 1$ if the duration is not censored ({\it
+  e.g.}, the subject dies during the study) or $c_i = 0$ if the
+duration is censored ({\it e.g.}, the subject is still alive at the
+end of the study).  If $c_i$ is omitted, all Y are assumed to be
+completed; that is, time defaults to 1 for all observations.
+
+\subsubsection{Input Values} 
+
+In addition to the standard inputs, {\tt zelig()} takes the following
+additional options for weibull regression:  
+\begin{itemize}
+\item {\tt robust}: defaults to {\tt FALSE}.  If {\tt TRUE}, {\tt
+zelig()} computes robust standard errors based on sandwich estimators
+(see \cite{Huber81} and \cite{White80}) based on the options in {\tt
+cluster}.
+\item {\tt cluster}:  if {\tt robust = TRUE}, you may select a
+variable to define groups of correlated observations.  Let {\tt x3} be
+a variable that consists of either discrete numeric values, character
+strings, or factors that define strata.  Then
+\begin{verbatim}
+> z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3", 
+                 model = "exp", data = mydata)
+\end{verbatim}
+means that the observations can be correlated within the strata defined by
+the variable {\tt x3}, and that robust standard errors should be
+calculated according to those clusters.  If {\tt robust = TRUE} but
+{\tt cluster} is not specified, {\tt zelig()} assumes that each
+observation falls into its own cluster.  
+\end{itemize}  
+
+\subsubsection{Example}
+
+Attach the sample data: 
+\begin{verbatim}
+> data(coalition)
+\end{verbatim}
+Estimate the model: 
+\begin{verbatim}
+> z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "weibull",
+                 data = coalition)
+\end{verbatim}
+View the regression output:  
+\begin{verbatim}
+> summary(z.out)
+\end{verbatim}
+Set the baseline values (with the ruling coalition in the minority)
+and the alternative values (with the ruling coalition in the majority)
+for X:
+\begin{verbatim}
+> x.low <- setx(z.out, numst2 = 0)
+> x.high <- setx(z.out, numst2 = 1)
+\end{verbatim}
+Simulate expected values ({\tt qi\$ev}) and first differences ({\tt qi\$fd}):
+\begin{verbatim}
+> s.out <- sim(z.out, x = x.low, x1 = x.high)
+> summary(s.out)
+> plot(s.out)
+\end{verbatim}
+
+\subsubsection{Model}
+Let $Y_i^*$ be the survival time for observation $i$. This variable
+might be censored for some observations at a fixed time $y_c$ such
+that the fully observed dependent variable, $Y_i$, is defined as
+\begin{equation*}
+  Y_i = \left\{ \begin{array}{ll}
+      Y_i^* & \textrm{if }Y_i^* \leq y_c \\
+      y_c & \textrm{if }Y_i^* > y_c 
+    \end{array} \right.
+\end{equation*}
+
+\begin{itemize}
+\item The \emph{stochastic component} is described by the distribution
+  of the partially observed variable $Y^*$.  We assume $Y_i^*$ follows
+  the Weibull distribution whose density function is given by
+  \begin{equation*}
+    f(y_i^*\mid \lambda_i, \alpha) = \frac{\alpha}{\lambda_i^\alpha}
+    y_i^{* \alpha-1} \exp \left\{ -\left( \frac{y_i^*}{\lambda_i}
+\right)^{\alpha} \right\}
+  \end{equation*}
+  for $y_i^* \ge 0$, the scale parameter $\lambda_i > 0$, and the shape
+  parameter $\alpha > 0$. The mean of this distribution is $\lambda_i
+  \Gamma(1 + 1 / \alpha)$. When $\alpha = 1$, the distribution reduces to
+  the exponential distribution (see Section~\ref{exp}).  (Note that
+the output from {\tt zelig()} parameterizes {\tt scale}$ = 1 / \alpha$.)
+
+In addition, survival models like the Weibull have three additional
+properties.  The hazard function $h(t)$ measures the probability of
+not surviving past time $t$ given survival up to $t$. In general,
+the hazard function is equal to $f(t)/S(t)$ where the survival
+function $S(t) = 1 - \int_{0}^t f(s) ds$ represents the fraction still
+surviving at time $t$.  The cumulative hazard function $H(t)$
+describes the probability of dying before time $t$.  In general,
+$H(t)= \int_{0}^{t} h(s) ds = -\log S(t)$.  In the case of the Weibull
+model,
+\begin{eqnarray*}
+h(t) &=& \frac{\alpha}{\lambda_i^{\alpha}} t^{\alpha - 1}  \\
+S(t) &=&  \exp \left\{ -\left( \frac{t}{\lambda_i} \right)^{\alpha} \right\} \\
+H(t) &=& \left( \frac{t}{\lambda_i} \right)^{\alpha}
+\end{eqnarray*}
+For the Weibull model, the hazard function $h(t)$ can increase or
+decrease monotonically over time.  
+
+\item The \emph{systematic component} $\lambda_i$ is modeled as
+  \begin{equation*}
+    \lambda_i = \exp(x_i \beta),
+  \end{equation*}
+  where $x_i$ is the vector of explanatory variables, and $\beta$ is
+  the vector of coefficients.
+  
+\end{itemize}
+
+\subsubsection{Quantities of Interest}
+
+\begin{itemize}
+\item The expected values ({\tt qi\$ev}) for the Weibull model are
+  simulations of the expected duration:
+\begin{equation*}
+E(Y) = \lambda_i \, \Gamma (1 + \alpha^{-1}),
+\end{equation*}
+given draws of $\beta$ and $\alpha$ from their sampling
+distributions. 
+
+\item The predicted value ({\tt qi\$pr}) is drawn from a distribution
+  defined by $(\lambda_i, \alpha)$.  
+
+\item The first difference ({\tt qi\$fd}) in expected value is
+\begin{equation*}
+\textrm{FD} = E(Y \mid x_1) - E(Y \mid x). 
+\end{equation*}
+
+\item In conditional prediction models, the average expected treatment
+  effect ({\tt att.ev}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      E[Y_i(t_i=0)] \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups. When
+    $Y_i(t_i=1)$ is censored rather than observed, we replace it with
+    a simulation from the model given available knowledge of the
+    censoring process.  Variation in the simulations are due to
+    uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual
+    expected value of $Y_i$ for observations in the treatment group,
+    under the assumption that everything stays the same except that
+    the treatment indicator is switched to $t_i=0$.
+
+\item In conditional prediction models, the average predicted treatment
+  effect ({\tt att.pr}) for the treatment group is 
+    \begin{equation*} \frac{1}{\sum_{i=1}^n t_i}\sum_{i:t_i=1}^n \left\{ Y_i(t_i=1) -
+      \widehat{Y_i(t_i=0)} \right\},
+    \end{equation*} 
+    where $t_i$ is a binary explanatory variable defining the
+    treatment ($t_i=1$) and control ($t_i=0$) groups.  When
+    $Y_i(t_i=1)$ is censored rather than observed, we replace it with
+    a simulation from the model given available knowledge of the
+    censoring process.  Variation in the simulations are due to
+    uncertainty in simulating $\widehat{Y_i(t_i=0)}$, the
+    counterfactual predicted value of $Y_i$ for observations in the
+    treatment group, under the assumption that everything stays the
+    same except that the treatment indicator is switched to $t_i=0$.
+\end{itemize}
+
+\subsubsection{Output Values}
+
+The output of each Zelig command contains useful information which you
+may view.  For example, if you run \texttt{z.out <- zelig(y \~\,
+  x, model = "weibull", data)}, then you may examine the available
+information in \texttt{z.out} by using \texttt{names(z.out)},
+see the {\tt coefficients} by using {\tt z.out\$coefficients}, and
+a default summary of information through \texttt{summary(z.out)}.
+Other elements available through the {\tt \$} operator are listed
+below.
+
+\begin{itemize}
+\item From the {\tt zelig()} output object {\tt z.out}, you may extract:
+   \begin{itemize}
+   \item {\tt coefficients}: parameter estimates for the explanatory
+     variables.
+   \item {\tt icoef}: parameter estimates for the intercept and ``scale''
+     parameter $1 / \alpha$.  
+   \item {\tt var}: the variance-covariance matrix.  
+   \item {\tt loglik}: a vector containing the log-likelihood for the
+     model and intercept only (respectively).
+   \item {\tt linear.predictors}: a vector of the
+     $x_{i}\beta$.
+   \item {\tt df.residual}: the residual degrees of freedom.
+   \item {\tt df.null}: the residual degrees of freedom for the null
+     model.
+   \item {\tt zelig.data}: the input data frame if {\tt save.data = TRUE}.  
+   \end{itemize}
+\item Most of this may be conveniently summarized using {\tt
+   summary(z.out)}.  From {\tt summary(z.out)}, you may
+ additionally extract: 
+   \begin{itemize}
+   \item {\tt table}: the parameter estimates with their
+     associated standard errors, $p$-values, and $t$-statistics.
+   \end{itemize}
+
+\item From the {\tt sim()} output object {\tt s.out}, you may extract
+  quantities of interest arranged as matrices indexed by simulation
+  $\times$ {\tt x}-observation (for more than one {\tt x}-observation).
+  Available quantities are:
+
+   \begin{itemize}
+   \item {\tt qi\$ev}: the simulated expected values for the specified
+     values of {\tt x}.
+   \item {\tt qi\$pr}: the simulated predicted values drawn from a
+     distribution defined by $(\lambda_i, \alpha)$.  
+   \item {\tt qi\$fd}: the simulated first differences between the
+     simulated expected values for {\tt x} and {\tt x1}.  
+   \item {\tt qi\$att.ev}: the simulated average expected treatment
+     effect for the treated from conditional prediction models.  
+   \item {\tt qi\$att.pr}: the simulated average predicted treatment
+     effect for the treated from conditional prediction models.  
+   \end{itemize}
+\end{itemize}
+
+\subsubsection{Contributors}
+
+The Weibull model is part of the survival library by Terry Therneau,
+ported to R by Thomas Lumley.  Advanced users may wish to refer to
+\texttt{help(survfit)} in the survival library, and 
+\begin{verse}
+\bibentry{VenRip02}.
+\end{verse}
+
+Sample data are from 
+\begin{verse}
+\bibentry{KinAltBur90}.  
+\end{verse}
+
+Kosuke Imai, Gary King, and Olivia Lau added Zelig functionality.  
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: t
+%%% End: 
+
+
+
+
+
+
+
+
+
+
+
diff --git a/inst/doc/newModels.tex b/inst/doc/newModels.tex
new file mode 100644
index 0000000..7900cef
--- /dev/null
+++ b/inst/doc/newModels.tex
@@ -0,0 +1,615 @@
+\chapter{Writing New Models} \label{s:new}
+
+With Zelig, writing a new model in R is straightforward.  (If you
+already have a model, see Chapter \ref{c:addingmodels} for how to include it in
+Zelig.)  With tools to streamline user inputs, writing a new model
+does not require a lot of programming knowledge, but lets developers
+focus on the model's math.  Generally, writing a new statistical
+procedure or model comes in orderly steps:  
+\begin{enumerate}
+\item Write down the mathematical model. Define the parameters that
+you need, grouping parameters into convenient vectors or matrices
+whenever possible (this will make your code clearer).  
+\item Write the code.  
+\item Test the code (usually using Monte Carlo data, where you know
+the true values being estimated ) and make sure
+that it works as expected. 
+\item Write some documentation explaining your model and the functions
+that run your model.  
+\end{enumerate}
+Somewhere between steps [1] and [2], you will need to translate input
+data into the mathematical notation that you used to write down the
+model.  Rather than repeating whole blocks of code, use functions to
+streamline the number of commands that users will need to run your
+model.  
+
+With more steps being performed by fewer commands, the inputs to these
+commands become more sophisticated.  The structure of those inputs
+actually matters quite a lot.  If your function has a convoluted
+syntax, it will be difficult to use, difficult to explain, and
+difficult to document.  If your function is easy to use and has an
+intuitive syntax, however, it will be easy to explain and document,
+which will make your procedure more accessible to all users.
+
+\section{Managing Statistical Model Inputs}
+\label{ui}  
+
+Most statistical models require a matrix of explanatory variables and
+a matrix of dependent variables.  Rather than have users create
+matrices themselves, R has a convenient user interface to create
+matrices of response and explanatory variables on the fly.  Users
+simply specify a {\tt formula} in the form of 
+\verb|dependent ~ explanatory variables|, and developers use the
+following functions to transform the formula into the appropriate
+matrices.  Let {\tt mydata} be a data frame.  
+\begin{verbatim}
+> formula <- y ~ x1 + x2                   # User input
+
+# Given the formula above, programmers can use the following standard commands
+> D <- model.frame(formula, data = mydata) # Subset & listwise deletion
+> X <- model.matrix(formula, data = D)     # Creates X matrix
+> Y <- model.response(D)                   # Creates Y matrix
+\end{verbatim}
+where 
+\begin{itemize}
+\item {\tt D} is a subset of {\tt mydata} that contains only the
+variables specified in the formula ({\tt y}, {\tt x1}, and {\tt x2})
+with listwise deletion performed on the subset data frame; 
+\item {\tt X} is a matrix that contains a column of 1's, and the
+explanatory variables {\tt x1} and {\tt x2} from {\tt D}; and
+\item {\tt Y} is a matrix containing the dependent variable(s) from
+{\tt D}.  
+\end{itemize}  
+Depending on the model, {\tt Y} may be a column vector, matrix, or
+other data structure.  
+
+\subsection{Describe the Statistical Model}  
+
+After setting up the $X$ matrix, the next step for most models will be
+to identify the corresponding vector of parameters.  For a single
+response variable model with no ancillary parameters, the standard R
+interface is quite convenient: given $X$, the model's parameters are
+simply $\beta$.
+
+There are very few models, however, that fall into this category.
+Even Normal regression, for example, has two sets of parameters
+$\beta$ and $\sigma^2$.  In order to make the R formula format more
+flexible, Zelig has an additional set of tools that lets you describe
+the inputs to your model (for multiple sets of parameters).
+
+After you have written down the statistical model, identify the
+parameters in your model.  With these parameters in mind, the first
+step is to write a {\tt describe.*()} function for your model.  If
+your model is called {\tt mymodel}, then the {\tt describe.mymodel()}
+function takes no arguments and returns a list with the following
+information:
+\begin{itemize}
+\item {\tt category}: a character string that describes the dependent
+variable.  See \Sref{categories} for the current list of available
+categories.  
+\item {\tt parameters}:  a list containing parameter sets used in your
+model.  For each parameter (e.g., theta), you need to provide the
+following information:  
+
+\begin{itemize}
+
+\item {\tt equations}: an integer number of equations for the
+parameter.  For parameters that can take, for example, two to four
+equations, use {\tt c(2, 4)}.  
+
+\item {\tt tagsAllowed}: a logical value ({\tt TRUE}/{\tt FALSE})
+specifying whether a given parameter allows constraints.  
+
+\item {\tt depVar}: a logical value ({\tt TRUE}/{\tt FALSE})
+specifying whether a parameter requires a corresponding dependent
+variable.  
+
+\item {\tt expVar}: a logical value ({\tt TRUE}/{\tt FALSE})
+specifying whether a parameter allows explanatory variables.  
+
+\end{itemize}
+\end{itemize}
+(See \Sref{describe.mymodel} for examples and additional arguments
+output by {\tt describe.mymodel()}.)
+
+\subsection{Single Response Variable Models: Normal Regression Model}
+\label{normal.regression}
+
+Let's say that you are trying to write a Normal regression model with
+stochastic component
+\begin{displaymath}
+\textrm{Normal}(y_i \mid \mu_i, \sigma^2) \; = \; \frac{1}{\sqrt{2 \pi} \sigma} \, \exp
+\left( -\left( \frac{(y_i - \mu_i)^2}{2 \sigma^2} \right) \right)
+\end{displaymath}
+with scalar variance parameter $\sigma^2 > 0$, and systematic component
+$E(Y_i) = \mu_i = x_i \beta$.  This implies two sets of parameters in your
+model, and the following {\tt describe.normal.regression()} function:
+\begin{verbatim}
+describe.normal.regression <- function() {
+  category <- "continuous"
+  mu <- list(equations = 1,              # Systematic component
+             tagsAllowed = FALSE,
+             depVar = TRUE,
+             expVar = TRUE)
+  sigma2 <- list(equations = 1,          # Scalar ancillary parameter
+                 tagsAllowed = FALSE,
+                 depVar = FALSE,
+                 expVar = FALSE)
+  pars <- list(mu = mu, sigma2 = sigma2)
+  list(category = category, parameters = pars)
+}
+\end{verbatim}
+
+To find the log-likelihood:  
+\begin{eqnarray*}
+\textrm{L }(\beta, \sigma^2 \mid y) & = & \prod_{1=1}^n
+       \textrm{Normal}(y_i \mid \mu_i, \sigma^2)\\
+    & = & \prod_{i=1}^n (2\pi\sigma^2)^{-1/2}\exp\left(\frac{-(y_i-\mu_i)^2}
+{2\sigma^2}\right)\\
+    & = &(2\pi\sigma^2)^{-n/2} \prod_{i=1}^n \exp\left(\frac{-(y_i-\mu_i)^2}
+{2\sigma^2}\right)\\
+    & = &(2\pi\sigma^2)^{-n/2} \prod_{i=1}^n \exp\left(\frac{-(y_i-x_i \beta)^2}
+{2\sigma^2}\right)\\
+\ln \textrm{L }(\beta, \sigma^2 \mid y) &=& -\frac{n}{2}\ln(2\pi\sigma^2)-
+\sum_{i=1}^n \frac{(y_i-x_i\beta)^2}{2\sigma^2}\\
+       &=& -\frac{n}{2}\ln(2\pi\sigma^2)-
+        \frac{1}{2\sigma^2}\sum_{i=1}^n (y_i-x_i\beta)^2 \\
+&\propto& -\frac{1}{2} \left( n \ln\sigma^2 + \frac{\sum_{i=1}^n
+(y_i-x_i\beta)^2}{\sigma^2} \right)
+\end{eqnarray*}
+In R code, this translates to:  
+\begin{verbatim}
+ll.normal <- function(par, X, Y, n, terms) {
+  beta <- parse.par(par, terms, eqn = "mu")             # [1]
+  gamma <- parse.par(par, terms, eqn = "sigma2")        # [2]
+  sigma2 <- exp(gamma)
+  -0.5 * (n * log(sigma2) + sum((Y - X %*% beta)^2 / sigma2))
+}
+\end{verbatim}
+At Comment [1] above, we use the function {\tt parse.par()}
+to pull out the vector of parameters {\tt beta} (which relate the
+systematic component $\mu_i$ to the explanatory variables $x_i$).  No
+matter how many covariates there are, the {\tt parse.par()} function
+can use {\tt terms} to pull out the appropriate parameters from {\tt par}.
+We also use {\tt parse.par()} at Comment [2] to pull out the scalar
+ancillary parameter that (after transformation) corresponds to the
+$\sigma^2$ parameter. 
+
+To optimize this function, simply type:  
+\begin{verbatim}
+out <- optim(start.val, ll.normal, control = list(fnscale = -1),
+             method = "BFGS", hessian = TRUE, X = X, Y = Y, terms = terms)
+\end{verbatim}
+where
+\begin{itemize}
+\item {\tt start.val} is a vector of starting values for {\tt par}.
+Use {\tt set.start()} to create starting values for all parameters,
+systematic and ancillary, in one step.  
+\item {\tt ll.normal} is the log-likelihood function derived above.
+\item {\tt "BFGS"} specifies unconstrained optimization using a
+  quasi-Newton method.
+\item {\tt control = list(fnscale = -1)} specifies that R should
+  maximize the function (omitting this causes R to minimize the
+  function by default).
+\item {\tt hessian = TRUE} instructs R to return the Hessian matrix
+  (from which you may calculate the variance-covariance matrix).
+\item {\tt X} and {\tt Y} are the matrix of explanatory variables and
+  vector of dependent variables, used in the {\tt ll.normal()}
+  function.
+\item {\tt terms} are meta-data constructed from the {\tt
+model.frame()} command.  
+\end{itemize}
+Please refer to the R-help for {\tt optim()} for more options.
+
+To make this procedure generalizable, we can write a function that
+takes a user-specified data frame and formula, and optional starting
+values for the optimization procedure:  
+\begin{verbatim}
+normal.regression <- function(formula, data, start.val = NULL, ...) {
+
+  fml <- parse.formula(formula, model = "normal.regression") # [1]
+  D <- model.frame(fml, data = data)
+  X <- model.matrix(fml, data = D)
+  Y <- model.response(D)
+  terms <- attr(D, "terms")
+  n <- nrow(X)
+
+  start.val <- set.start(start.val, terms)
+
+  res <- optim(start.val, ll.normal, method = "BFGS",        
+               hessian = TRUE, control = list(fnscale = -1),
+               X = X, Y = Y, n = n, terms = terms, ...)      # [2]
+
+  fit <- model.end(res, D)                                   # [3]
+  fit$n <- n
+  class(fit) <- "normal"                                     # [4]
+  fit                                                        
+}
+\end{verbatim} %$
+The following comments correspond to the bracketed numbers above:  
+\begin{enumerate}
+\item The {\tt parse.formula()} command looks for the {\tt
+describe.normal.regression()} function, which changes the
+user-specified formula into the following format:  
+\begin{verbatim}
+list(mu = formula,         # where `formula' was specified by the user
+     sigma = ~ 1)
+\end{verbatim}
+\item The {\tt \dots} here indicate that if the user enters any
+additional arguments when calling {\tt normal.regression()}, that
+those arguments should go to the {\tt optim()} function.  
+\item The {\tt model.end()} function takes the optimized output and
+the listwise deleted data frame {\tt D} and creates an object that
+will work with {\tt setx()}.  
+\item Choose a class for your model output so that you will be able to
+write an appropriate {\tt summary()}, {\tt param()}, and {\tt qi()}
+function for your model.  
+\end{enumerate}
+
+\subsection{Multivariate models: Bivariate Normal example}\label{bivariate.probit}
+
+Most common models have one systematic component.  For $n$
+observations, the systematic component varies over observations $i = 1,
+\dots, n$.  In the case of the Normal regression model, the systematic
+component is $\mu_i$ ($\sigma^2$ is not estimated as a function of
+covariates).
+
+In some cases, however, your model may have more than one systematic
+component.  In the case of bivariate probit, we have a dependent
+variable $Y_i = (Y_{i1}, Y_{i2})$ observed as (0,0), (1,0), (0,1), or
+(1,1) for $i = 1, \dots, n$.  Similar to a single-response probit model,
+the stochastic component is described by two latent (unobserved)
+continuous variables ($Y_{i1}^*$, $Y_{i2}^*$) which follow the
+bivariate Normal distribution:
+\begin{eqnarray*}
+  \left ( \begin{array}{c} 
+      Y_{i1}^* \\
+      Y_{i2}^* 
+    \end{array}
+  \right ) &\sim &  
+  \textrm{Normal} \left \{ \left ( 
+      \begin{array}{c}
+        \mu_{i1} \\ \mu_{i2}
+      \end{array} \right ), \left( \begin{array}{cc}
+                 1 & \rho \\
+                 \rho & 1 
+                 \end{array} \right) \right\},
+\end{eqnarray*}
+where for $j = 1, 2$, $\mu_{ij}$ is the mean for $Y_{ij}^*$ and $\rho$ is a
+correlation parameter. The following observation mechanism links the
+observed dependent variables, $Y_{ij}$, with these latent variables
+\begin{eqnarray*}
+Y_{ij} & = & \left \{ \begin{array}{cc}
+                   1 & {\rm if} \; Y_{ij}^* \ge 0, \\
+                   0 & {\rm otherwise.}
+                   \end{array} 
+                   \right.
+\end{eqnarray*}
+
+The systemic components for each observation are 
+  \begin{eqnarray*}
+    \mu_{ij} & = & x_{ij} \beta_j \quad {\rm for} \quad j = 1, 2, \\
+    \rho & = & \frac{\exp(x_{i3} \beta_3) - 1}{\exp(x_{i3} \beta_3) + 1}.
+\end{eqnarray*}
+In the default specification, $\rho$ is a scalar
+(such that $x_{i3}$ only contains an intercept term).  
+
+If so, we have two sets of parameters: $\mu_{i} = (\mu_{i1},
+\mu_{i2})$ and $\rho$.  This implies the following {\tt
+describe.bivariate.probit()} function:
+\begin{verbatim}
+describe.bivariate.probit <- function() {
+  category <- "dichotomous"
+  package <- list(name = "mvtnorm",       # Required package and 
+                  version = "0.7")        #  minimum version number
+  mu <- list(equations = 2,               # Systematic component has 2
+             tagsAllowed = TRUE,          #  required equations
+             depVar = TRUE, 
+             expVar = TRUE), 
+  rho <- list(equations = 1,              # Optional systematic component
+             tagsAllowed = FALSE,         #   (estimated as an ancillary
+             depVar = FALSE,              #    parameter by default)
+             expVar = TRUE), 
+  pars <- parameters(mu = mu, rho = rho)
+  list(category = category, package = package, parameters = pars)
+}
+\end{verbatim}
+
+Since users may choose different explanatory variables to parameterize
+$\mu_{i1}$ and $\mu_{i2}$ (and sometimes $\rho$), the model requires a
+minimum of \emph{two} formulas.  For example,
+\begin{verbatim}
+formulae <- list(mu1 = y1 ~ x1 + x2,                         # User input
+                 mu2 = y2 ~ x2 + x3)
+fml <- parse.formula(formulae, model = "bivariate.probit")   # [1]
+D <- model.frame(fml, data = mydata)
+X <- model.matrix(fml, data = D)
+Y <- model.response(D)
+\end{verbatim}
+At comment [1], {\tt parse.formula()} finds the {\tt
+describe.bivariate.probit()} function and parses the formulas
+accordingly.  
+
+If $\rho$ takes covariates (and becomes a systematic component rather
+than an ancillary parameter), there can be three sets of explanatory variables:  
+\begin{verbatim}
+formulae <- list(mu1 = y1 ~ x1 + x2, 
+                 mu2 = y2 ~ x2 + x3, 
+                 rho = ~ x4 + x5) 
+\end{verbatim}
+
+From the perspective of the programmer, a nearly identical
+framework works for both single and multiple equation
+models.  The ({\tt parse.formula()}) line changes the class
+of {\tt fml} from {\tt "list"} to {\tt "multiple"} and hence ensures
+that {\tt model.frame()} and {\tt model.matrix()} go to the
+appropriate methods.  {\tt D}, {\tt X} , and {\tt Y} are analogous to their
+single equation counterparts above:
+\begin{itemize}
+\item  {\tt D} is the subset of {\tt mydata} containing the variables
+{\tt y1}, {\tt y2}, {\tt x1}, {\tt x2}, and {\tt x3} with listwise
+deletion performed on the subset; 
+\item {\tt X} is a matrix corresponding to the explanatory variables,
+in one of three forms discussed below (see \Sref{constraints}).  
+\item {\tt Y} is an $n \times J$  matrix (where $J=2$ here) with
+columns ({\tt y1}, {\tt y2}) corresponding to the outcome variables on
+the left-hand sides of the formulas.  
+\end{itemize}
+
+Given for the bivariate probit probability density described above,
+the likelihood is:
+\begin{displaymath}
+L(\mathbf{\pi} | Y_i) = \prod_{i=1}^n 
+                    \pi_{00}^{\textrm{I}\{Y_i = (0,0)\}}
+                    \pi_{10}^{\textrm{I}\{Y_i = (1,0)\}}
+                    \pi_{01}^{\textrm{I}\{Y_i = (0,1)\}}
+                    \pi_{11}^{\textrm{I}\{Y_i = (1,1)\}}
+\end{displaymath}
+where I is an indicator function and 
+\begin{itemize}
+\item $\pi_{00} = \int_{-\infty}^0 \int_{-\infty}^0 \textrm{Normal}(Y_{i1}^*, Y_{i2}^* \mid
+\mu_{i1}, \mu_{i2}, \rho) dY_{i2}^* dY_{i1}^*$
+\item $\pi_{10} = \int_0^{\infty} \int_{-\infty}^0 \textrm{Normal}(Y_{i1}^*, Y_{i2}^* \mid
+\mu_{i1}, \mu_{i2}, \rho) dY_{i2}^* dY_{i1}^*$
+\item $\pi_{01} = \int_{-\infty}^0 \int_0^{\infty} \textrm{Normal}(Y_{i1}^*, Y_{i2}^* \mid
+\mu_{i1}, \mu_{i2}, \rho) dY_{i2}^* dY_{i1}^*$
+\item $\pi_{11} = 1-\pi_{00}-\pi_{10}-\pi_{01}$
+\end{itemize}
+This implies the following log-likelihood:  
+\begin{eqnarray*}
+\log L(\mathbf{\pi} | Y_i) &=& \sum_{i=1}^n \textrm{I}\{Y_i = (0,0)\} \log\pi_{00}
++ \textrm{I}\{Y_i = (1,0)\} \log \pi_{10} \\
+&& \quad \quad + \textrm{I}\{Y_i = (0,1)\} \log \pi_{01}
++ \textrm{I}\{Y_i = (1,1)\} \log \pi_{11}
+\end{eqnarray*}
+(For the corresponding R code, see \Sref{bivariate.probit.llik} below.)  
+
+\section{Easy Ways to Manage Matrices}
+\label{constraints}
+
+Most statistical methods relate explanatory variables $x_i$ to a
+dependent variable of interest $y_i$ for each observation $i = 1,
+\dots, n$.  Let $\beta$ be a set of parameters that correspond to each
+column in $X$, which is an $n \times k$ matrix with rows $x_i$.  For a
+single equation model, the linear predictor is
+\begin{equation*}
+\eta_i = x_i \beta = \beta_0 + \beta_1 x_{i1} +
+\beta_2 x_{i2} + \dots + \beta_k x_{ik}
+\end{equation*}
+Thus, $\eta$ is the set of $\eta_i$ for $i = 1, \dots, n$ and is
+usually represented as an $n \times 1$ matrix.
+
+For a two equation model such as bivariate probit, the linear
+predictor becomes a matrix with columns corresponding to each
+dependent variable $(y_{1i}, y_{2i})$:
+\begin{equation*}
+\eta_i = (\eta_{i1}, \eta_{i2}) = (x_{i1} \beta_1, x_{i2} \beta_2)
+\end{equation*}
+With $\eta$ as an $n \times 2$ matrix, we now have a few choices as to
+how to create the linear predictor:
+\begin{enumerate}
+\item An {\bf intuitive} layout, which stacks matrices of explanatory
+variables, provides an easy visual representation of the relationship
+between explanatory variables and coefficients; 
+\item A {\bf computationally-efficient} layout, which takes advantage
+of computational vectorization; and
+\item A {\bf memory-saving} layout, which reduces the overall size of the
+$X$ and $\beta$ matrices.
+\end{enumerate}  
+Using the simple tools described in this section, you can pick the
+best matrix management method for your model.  
+
+In addition, the way in which $\eta$ is created also affects the
+way parameters are estimated.  Let's say that you want two parameters
+to have the same effect in different equations.  By setting up $X$ and
+$\beta$ in a certain way, you can let users set constraints across
+parameters.  Continuing the bivariate probit example above, let the
+model specification be:
+\begin{verbatim}
+formulae <- list(mu1 = y1 ~ x1 + x2 + tag(x3, "land"), 
+                 mu2 = y2 ~ x3 + tag(x4, "land"))
+\end{verbatim}
+where {\tt tag()} is a special function that constrains variables to
+have the same effect across equations.  Thus, the coefficient for {\tt
+x3} in equation {\tt mu1} is constrained to be equal to the
+coefficient for {\tt x4} in equation {\tt mu2}, and this effect is
+identified as the ``land'' effect in both equations.  In order to
+consider constraints across equations, the structure of both $X$ and
+$\beta$ matter.
+
+\subsection{The Intuitive Layout}  
+
+A stacked matrix of $X$ and vector $\beta$ is probably the most
+visually intuitive configuration.  Let $J = 2$ be the number of
+equations in the bivariate probit model, and let $v_t$ be the total
+number of unique covariates in both equations.  Choosing {\tt
+model.matrix(\dots, shape = "stacked")} yields a $(Jn \times v_t) =
+(2n \times 6)$ matrix of explanatory variables.  Again, let $x_1$ be
+an $n \times 1$ vector representing variable {\tt x1}, $x_2$ {\tt x2},
+and so forth.  Then
+\begin{equation*}
+X = \left (\begin{array}{cccccc}
+1 & 0 & x_1 & x_2 & 0   & x_3  \\ 
+0 & 1 & 0   & 0   & x_3 & x_4
+\end{array} \right) 
+\end{equation*}
+Correspondingly, $\beta$ is a vector with elements
+\begin{equation*}
+(\beta_0^{\mu_1} \; \beta_0^{\mu_2} \; \beta_{x_1}^{\mu_1} \;
+\beta_{x_2}^{\mu_1} \; \beta_{x_3}^{\mu_2} \; \beta_{\textrm{land}})\prime
+\end{equation*}
+where $\beta_0^j$ are the intercept terms for equation $j = \{\mu_1,
+\mu_2\}$.  Since $X$ is $(2n \times 6)$ and $\beta$ is $(6 \times 1)$, the
+resulting linear predictor $\eta$ is also stacked into a $(2n \times
+1)$ matrix.  Although difficult to manipulate (since observations are
+indexed by $i$ and $2i$ for each $i = 1, \dots, n$ rather than just
+$i$), it is easy to see that we have turned the two equations into one
+big $X$ matrix and one long vector $\beta$, which is directly
+analogous to the familiar single-equation $\eta$.
+
+\subsection{The Computationally-Efficient Layout}
+
+Choosing array $X$ and vector $\beta$ is probably the the most
+computationally-efficient configuration: {\tt
+model.matrix(\dots, shape = "array")} produces an $n \times k_t
+\times J$ array where $J$ is the total number of equations and $k_t$
+is the total number of parameters across all the equations.  Since some
+parameter values may be constrained across equations, $k_t \leq
+\sum_{j=1}^J k_j$.  If a variable is not in a certain equation, it is
+observed as a vector of 0s.  With this option, each $i = 1, \dots, n$
+$x_i$ matrix becomes:
+\begin{equation*}
+\left( \begin{array}{ccccccc}
+1 & 0 & x_{i1} & x_{i2} & 0      & x_{i3} \\
+0 & 1 & 0      & 0      & x_{i3} & x_{i4}
+\end{array} \right) 
+\end{equation*}
+By stacking each of these $x_i$ matrices along the first dimension,
+we get $X$ as an array with dimensions $n \times k_t \times J$.  
+
+Correspondingly, $\beta$ is a vector with elements
+\begin{equation*}
+(\beta_0^{\mu_1} \; \beta_0^{\mu_2} \; \beta_{x_1}^{\mu_1} \;
+\beta_{x_2}^{\mu_1} \; \beta_{x_3}^{\mu_2} \; \beta_{\textrm{land}})\prime
+\end{equation*}
+To multiply the $X$ array with dimensions $(n \times 6 \times 2)$ and
+the $(6 \times 1)$ $\beta$ vector, we \emph{vectorize} over equations
+as follows:  
+\begin{verbatim}
+eta <- apply(X, 3, '%*%', beta) 
+\end{verbatim} 
+The linear predictor {\tt eta} is therefore a $(n \times 2)$ matrix.  
+
+\subsection{The Memory-Efficient Layout}  
+
+Choosing a ``compact'' $X$ matrix and matrix $\beta$ is probably the most
+memory-efficient configuration: {\tt model.matrix(\dots,
+shape = "compact")} (the default) produces an $n \times v$ matrix,
+where $v$ is the number of unique variables (5 in this
+case)\footnote{Why 5? In addition to the intercept term (a variable
+which is the same in either equation, and so counts only as one
+variable), the \emph{unique} variables are $x_1$, $x_2$, $x_3$, and
+$x_4$.} in all of the equations.  Let $x_1$ be an $n \times 1$ vector
+representing variable {\tt x1}, $x_2$ {\tt x2}, and so forth.
+\begin{eqnarray*}
+X = (1 \; x_1 \; x_2 \; x_3 \; x_4) & & \beta = \left( \begin{array}{cc}
+
+	   \beta_0^{\mu_1}       & \beta_0^{\mu_2} \\
+\beta_{x_1}^{\mu_1}       & 0 \\
+\beta_{x_2}^{\mu_1}       & 0 \\
+\beta_{\textrm{land}} & \beta_{x_3}^{\mu_2} \\
+0                     & \beta_{\textrm{land}}
+\end{array} \right) 
+\end{eqnarray*}
+The $\beta_{\textrm{land}}$ parameter is used twice to implement the
+constraint, and the number of empty cells is minimized by implementing
+the constraints in $\beta$ rather than $X$.  Furthermore, since $X$ is
+$(n \times 5)$ and $\beta$ is $(5 \times 2)$, $X\beta = \eta$ is $n
+\times 2$.
+
+\subsection{Interchanging the Three Methods}  
+\label{bivariate.probit.llik} 
+
+Continuing the bivariate probit example above, we only need to modify
+a few lines of code to put these different schemes into effect.  Using the
+default (memory-efficient) options, the log-likelihood is: 
+\begin{verbatim}
+bivariate.probit <- function(formula, data, start.val = NULL, ...) {
+  fml <- parse.formula(formula, model = "bivariate.probit")
+  D <- model.frame(fml, data = data)
+  X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))       # [1]
+  Xrho <- model.matrix(fml, data = D, eqn = "rho")
+  Y <- model.response(D)
+  terms <- attr(D, "terms")
+  start.val <- set.start(start.val, terms)
+  start.val <- put.start(start.val, 1, terms, eqn = "rho")
+
+  log.lik <- function(par, X, Y, terms) {
+    Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))         # [2]
+    gamma <- parse.par(par, terms, eqn = "rho")
+    rho <- (exp(Xrho %*% gamma) - 1) / (1 + exp(Xrho %*% gamma))
+    mu <- X %*% Beta                                             # [3]
+    llik <- 0
+    for (i in 1:nrow(mu)){
+      Sigma <- matrix(c(1, rho[i,], rho[i,], 1), 2, 2)
+      if (Y[i,1]==1)
+        if (Y[i,2]==1)
+          llik <- llik + log(pmvnorm(lower = c(0, 0), upper = c(Inf, Inf), 
+                                     mean = mu[i,], corr = Sigma))
+        else
+          llik <- llik + log(pmvnorm(lower = c(0, -Inf), upper = c(Inf, 0), 
+                                     mean = mu[i,], corr = Sigma))
+      else
+        if (Y[i,2]==1)
+          llik <- llik + log(pmvnorm(lower = c(-Inf, 0), upper = c(0, Inf),
+                                     mean = mu[i,], corr = Sigma))
+        else
+          llik <- llik + log(pmvnorm(lower = c(-Inf, -Inf), upper = c(0, 0), 
+                                     mean = mu[i,], corr = Sigma))
+        }
+    return(llik)
+  }
+  res <- optim(start.val, log.lik, method = "BFGS",
+               hessian = TRUE, control = list(fnscale = -1),
+               X = X, Y = Y, terms = terms, ...)
+  fit <- model.end(res, D)
+  class(fit) <- "bivariate.probit"
+  fit
+}
+\end{verbatim}  
+
+If you find that the default (memory-efficient) method isn't the best
+way to run your model, you can use either the intuitive option or the
+computationally-efficient option by changing just a few lines of code
+as follows:
+\begin{itemize}  
+\item {\bf Intuitive option}  At Comment [1]:
+\begin{verbatim}
+X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2"))
+\end{verbatim}
+and at Comment [2],
+\begin{verbatim}
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+\end{verbatim}
+The line at Comment [3] remains the same as in the original version.  
+
+\item {\bf Computationally-efficient option} Replace the line at Comment [1] with
+\begin{verbatim}
+X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
+\end{verbatim}
+At Comment [2]:
+\begin{verbatim}
+Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
+\end{verbatim}
+At Comment [3]:  
+\begin{verbatim}
+mu <- apply(X, 3, '%*%', Beta)
+\end{verbatim}
+\end{itemize}  
+
+Even if your optimizer calls a C or FORTRAN routine, you can use
+combinations of {\tt model.matrix()} and {\tt parse.par()} to set up
+the data structures that you need to obtain the linear predictor (or
+your model's equivalent) before passing these data structures to your
+optimization routine.  
+
diff --git a/inst/doc/refman.tex b/inst/doc/refman.tex
new file mode 100644
index 0000000..43b5abb
--- /dev/null
+++ b/inst/doc/refman.tex
@@ -0,0 +1,186 @@
+\chapter{Main Commands}\label{s:main}
+
+Help for each command in Zelig and R is available through {\tt
+  help.zelig()}.  For example, typing {\tt help.zelig(setx)} will
+launch a web browser with the appropriate reference manual page for
+the {\tt setx()} command.  (Occasionally, you may need to use, for example, {\tt
+  help(print)} rather than {\tt help.zelig(print)}, to access the R
+  help page instead of the default Zelig help page.)  
+
+\input{commands/zelig}
+
+\include{commands/setx}
+
+\include{commands/sim}
+
+\include{commands/summary}
+
+\include{commands/plot.zelig}
+
+\include{commands/print}
+
+\include{commands/replicate}
+
+\chapter{Supplementary Commands}
+
+\input{commands/matchit}
+
+\input{commands/mi}
+
+\include{commands/plot.ci}
+
+\include{commands/rocplot}
+
+\include{commands/ternaryplot}
+
+\include{commands/ternarypoints}
+
+\chapter{Models Zelig Can Run}\label{s:model.details}
+
+This section describes the mathematical components of the models
+supported by Zelig, using whenever possible the classification and
+notation of \cite{King89}.  Most models have a \emph{stochastic
+  component} (probability density given certain parameters) and
+a \emph{systematic component} (deterministic functional form that
+specifies how one or more of the parameters varies over the observed
+values $y_i$ as a function of the explanatory variables $x_i$).
+
+Let $Y_i$ be a random outcome variable, realized as $i = 1, \dots, n$
+observations $y_i$.  For the probability density $f(\cdot)$ with
+systematic feature $\theta_i$ varying over $i$ and a scalar ancillary
+parameter $\alpha$ (constant over $i$), the stochastic component is
+given by
+\begin{equation*}
+Y_i \sim f(y_i \mid \theta_i, \alpha).
+\end{equation*}
+
+For a functional form $g(\cdot)$, $k$ explanatory variables $X_i$, and
+effect parameters $\beta$, the systematic component is:  
+\begin{equation*}
+\theta_i = g(x_i, \beta).
+\end{equation*}
+
+Using the definitions of \hlink{King, Tomz, and Wittenberg,
+    2000}{http://gking.harvard.edu/files/abs/making-abs.shtml},
+  \nocite{KinTomWit00}
+Zelig generates at least two quantities of interest:   
+\begin{itemize}
+\item The predicted value is a random draw from the stochastic component
+  given random draws of $\beta$ and $\alpha$ from their sampling (or
+  posterior) distribution.  
+\item The expected value is the \emph{mean} of the stochastic component
+  given random draws of $\beta$ and $\alpha$ from their sampling (or
+  posterior) distributions.  For computational efficiency, Zelig
+  deterministically calculates the expected values from the simulated
+  parameters whenever possible. 
+\end{itemize}
+
+Both the predicted values and expected values produced by Zelig can be
+displayed as histograms or density estimates (to summarize the full
+sampling or posterior density), or summarized with confidence
+intervals (by sorting the simulations and taking the 5th and 95th
+percentile values for a 90\% confidence interval for example),
+standard errors (by taking the standard deviation of the simulations),
+or point estimates (by averaging the simulations).  The point estimate
+of predicted and expected values are the same only in linear models.
+In almost all situations, simulations from predicted values have more
+variance than expected values.  As the number of simulations increases
+the distribution of the expected values tends toward a constant; the
+distribution of the predicted values does not collapse as the number
+of simulations increases.
+
+\include{models/arima}
+
+%\include{models/beta}
+
+\include{models/blogit}
+
+\include{models/bprobit}
+
+\include{models/ei.dynamic}
+
+\include{models/ei.hier}
+
+\include{models/eiRxC}
+
+\include{models/exp}
+
+\include{models/factor.bayes}
+
+\include{models/factor.mix}
+
+\include{models/factor.ord}
+
+\include{models/gamma}
+
+\include{models/irt1d}
+
+\include{models/irtkd}
+
+\include{models/logit}
+
+\include{models/logit.bayes}
+
+\include{models/lognormal}
+
+\include{models/ls}
+
+\include{models/mlogit}
+
+\include{models/mlogit.bayes}
+
+% \include{models/mloglm}
+
+\include{models/negbin}
+
+\include{models/normal}
+
+\include{models/normal.bayes}
+
+\include{models/ologit}
+
+\include{models/oprobit}
+
+\include{models/oprobit.bayes}
+
+\include{models/poisson}
+
+\include{models/poisson.bayes}
+
+\include{models/probit}
+
+\include{models/probit.bayes}
+
+\include{models/relogit}
+
+\include{models/tobit}
+
+\include{models/tobit.bayes}
+
+\include{models/weibull}
+
+\chapter{Commands for Programmers and Contributors}
+
+\input{commands/describe.mymodel}
+
+\include{commands/model.end}
+
+\include{commands/model.frame.multiple}
+
+\include{commands/model.matrix.multiple}
+
+\include{commands/parse.formula}
+
+\include{commands/parse.par}
+
+\include{commands/put.start}
+
+\include{commands/set.start}
+
+\include{commands/tag}
+
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "zelig"
+%%% End: 
diff --git a/inst/doc/syntax.tex b/inst/doc/syntax.tex
new file mode 100644
index 0000000..0a992e2
--- /dev/null
+++ b/inst/doc/syntax.tex
@@ -0,0 +1,588 @@
+\chapter{Data Analysis Commands}\label{c:R}
+
+\section{Command Syntax}
+
+Once R is installed, you only need to know a few basic elements to get
+started.  It's important to remember that R, like any spoken
+language, has rules for proper syntax.  Unlike English, however, the
+rules for intelligible R are small in number and quite precise (see
+\Sref{s:syntax}).
+
+\subsection{Getting Started}
+
+\begin{enumerate}
+\item To start R under Linux or Unix, type R at the terminal prompt or
+  M-x R under ESS.
+\item The R prompt is {\tt >}.
+\item Type commands and hit enter to execute.  (No additional
+  characters, such as semicolons or commas, are necessary at the end
+  of lines.)
+\item To quit from R, type \texttt{q()} and press enter.
+\item The {\tt \#} character makes R ignore the rest of the line, and
+  is used in this document to comment R code.
+\item We highly recommend that you make a separate working directory
+  or folder for each project.
+\item Each R session has a workspace, or working memory, to store the
+  \emph{objects} that you create or input.  These objects may be:
+\begin{enumerate} 
+\item \emph{values}, which include numerical, integer, character, and
+  logical values;
+\item \emph{data structures} made up of variables (vectors), matrices,
+  and data frames; or
+\item \emph{functions} that perform the desired tasks on
+  user-specified values or data structures.
+\end{enumerate}
+\end{enumerate}
+
+After starting R, you may at any time use Zelig's built-in help
+function to access on-line help for any command.  To see help for all
+Zelig commands, type {\tt help.zelig(command)}, which will take you
+to the help page for all Zelig commands.  For help with a specific
+Zelig or R command substitute the name of the command for the generic
+{\tt command}.  For example, type {\tt help.zelig(logit)} to view
+help for the logit model.
+
+\subsection{Details}\label{s:syntax}.
+
+Zelig uses the syntax of R, which has several essential elements:
+\begin{enumerate}
+\item R is case sensitive.  \texttt{Zelig}, the package or library, is
+  not the same as \texttt{zelig}, the command.
+  
+\item R functions accept user-defined arguments: while some arguments
+  are required, other optional arguments modify the function's default
+  behavior.  Enclose arguments in parentheses and separate multiple
+  arguments with commas.  For example, {\tt print(x)} or {\tt print(x,
+    digits = 2)} prints the contents of the object {\tt x} using the
+  default number of digits or rounds to two digits to the right of the decimal
+  point, respectively.  You may nest commands as long as each has its
+  own set of parentheses: \texttt{log(sqrt(5))} takes the square root
+  of 5 and then takes the natural log.
+  
+\item The {\tt <-} operator takes the output of the function on the
+  right and saves them in the named object on the left.  For example,
+  {\tt z.out <- zelig(\dots)} stores the output from {\tt zelig()} as
+  the object {\tt z.out} in your working memory.  You may use {\tt
+    z.out} as an argument in other functions, view the output by
+  typing {\tt z.out} at the R prompt, or save {\tt z.out} to a file
+  using the procedures described in \Sref{s:save}.
+  
+\item You may name your objects anything, within a few constraints:
+    \begin{itemize}
+    \item You may only use letters (in upper or lower case) and
+      periods to punctuate your variable names.
+    \item You may \emph{not} use any special characters (aside from the
+      period) or spaces to punctuate your variable names.
+    \item Names cannot begin with numbers.  For example, R will not
+      let you save an object as \texttt{1997.election} but will let
+      you save \texttt{election.1997}.
+    \end{itemize}
+    
+  \item Use the {\tt names()} command to see the contents of R
+    objects, and the {\tt \$} operator to extract elements from R
+    objects.  For example:
+\begin{verbatim}
+# Run least squares regression and save the output in working memory:  
+> z.out <- zelig(y ~ x1 + x2, model = "ls", data = mydata)
+# See what's in the R object: 
+> names(z.out)
+ [1] "coefficients"  "residuals"  "effects"  "rank"
+# Extract and display the coefficients in z.out: 
+> z.out$coefficients 
+\end{verbatim} %$
+    
+  \item All objects have a class designation which tells R how to
+    treat it in subsequent commands.  An object's class is generated
+    by the function or mathematical operation that created it.
+    
+  \item To see a list of all objects in your current workspace, type:
+    \texttt{ls()}.  You can remove an object permanently from memory
+    by typing \texttt{remove(goo)} (which deletes the object {\tt
+      goo}), or remove all the objects with {\tt remove(list = ls())}.
+  
+  \item To run commands in a batch, use a text editor (such as the
+  Windows R script editor or emacs) to compose your R commands, and
+  save the file with a {\tt .R} file extension in your working
+  directory.  To run the file, type \texttt{source("Code.R")} at the R
+  prompt.
+\end{enumerate}
+
+If you encounter a syntax error, check your spelling, case,
+parentheses, and commas.  These are the most common syntax errors, and
+are easy to detect and correct with a little practice.  If you
+encounter a syntax error in batch mode, R will tell you the line on
+which the syntax error occurred.
+
+\section{Data Sets}
+
+\subsection{Data Structures}
+
+Zelig uses only three of R's many data structures: 
+\begin{enumerate}
+\item A {\bf variable} is a one-dimensional vector of length $n$.
+\item A {\bf data frame} is a rectangular matrix with $n$ rows and $k$
+  columns.  Each column represents a variable and each row an
+  observation.  Each variable may have a different class.  (See
+  \Sref{variable.classes} for a list of classes.)  You may refer to
+  specific variables from a data frame using, for example, {\tt
+    data\$variable}.
+\item A {\bf list} is a combination of different data structures.  For
+  example, {\tt z.out} contains both {\tt coefficients} (a vector) and
+  {\tt data} (a data frame).  Use {\tt names()} to view the elements
+  available within a list, and the {\tt \$} operator to refer to an
+  element in a list.
+\end{enumerate}
+
+For a more comprehensive introduction, including ways to manipulate
+these data structures, please refer to Chapter \ref{a:R}.
+
+\subsection{Loading Data}\label{load.data}
+
+Datasets in Zelig are stored in ``data frames.'' In this section, we
+explain the standard ways to load data from disk into memory, how to
+handle special cases, and how to verify that the data you loaded is
+what you think it is.
+
+\subsubsection*{Standard Ways to Load Data}
+
+Make sure that the data file is saved in your working directory.  You
+can check to see what your working directory is by starting R, and
+typing {\tt getwd()}.  If you wish to use a different directory as
+your starting directory, use \verb|setwd("dirpath")|, where
+\verb|"dirpath"| is the full directory path of the directory you would
+like to use as your working directory.  
+
+After setting your working directory, load data using one of the
+following methods:  
+
+\begin{enumerate}
+\item If your dataset is in a \textbf{tab- or space-delimited .txt
+    file}, use \texttt{read.table("mydata.txt")}
+\item If your dataset is \textbf{a comma separated table}, use
+  \texttt{read.csv("mydata.csv")}.
+\item To import {\bf SPSS, Stata, and other data files}, use the
+  foreign package, which automatically preserves field characteristics
+  for each variable.  Thus, variables classed as dates in Stata are
+  automatically translated into values in the date class for R.  For
+  example:
+\begin{verbatim}
+> library(foreign)                           # Load the foreign package.  
+> stata.data <- read.dta("mydata.dta")       # For Stata data.
+> spss.data <- read.spss("mydata.sav", to.data.frame = TRUE) # For SPSS.
+\end{verbatim}
+\item To load data in R format, use {\tt load("mydata.RData")}.  
+\item For sample data sets included with R packages such as Zelig, you
+  may use the {\tt data()} command, which is a shortcut for loading
+  data from the sample data directories.  Because the locations of
+  these directories vary by installation, it is extremely difficult to
+  locate sample data sets and use one of the three preceding methods;
+  {\tt data()} searches all of the currently used packages and loads
+  sample data automatically.  For example:
+\begin{verbatim}
+> library(Zelig)                              # Loads the Zelig library. 
+> data(turnout)                               # Loads the turnout data.
+\end{verbatim}
+\end{enumerate}
+
+\subsubsection*{Special Cases When Loading Data}
+
+These procedures apply to any of the above {\tt read} commands:
+\begin{enumerate}
+\item If your file uses the \textbf{first row to identify variable
+    names}, you should use the option \texttt{header = TRUE} to import
+  those field names.  For example,
+\begin{verbatim}  
+> read.csv("mydata.csv", header = TRUE)
+\end{verbatim}
+  will read the words in the first row as the variable names and the
+  subsequent rows (each with the same number of values as the first)
+  as observations for each of those variables. If you have additional
+  characters on the last line of the file or fewer values in one of
+  the rows, you need to edit the file before attempting to read the
+  data.
+\item The R missing value code is \texttt{NA}.  If this value is in
+  your data, R will recognize your missing values as such.  If you
+  have instead used a place-holder value (such as -9) to represent
+  missing data, you need to tell R this on loading the data:
+\begin{verbatim}
+> read.table("mydata.tab", header = TRUE, na.strings = "-9")
+\end{verbatim}
+  Note: You must enclose your place holder values in quotes.
+\item Unlike Windows, the file extension in R does not determine the
+  default method for dealing with the file.  For example, if your data
+  is tab-delimited, but saved as a {\tt .sav} file,
+  \texttt{read.table("mydata.sav")} will load your data into R.
+\end{enumerate}
+
+\subsubsection*{Verifying You Loaded The Data Correctly}
+
+Whichever method you use, try the \texttt{names()}, \texttt{dim()},
+and \texttt{summary()} commands to verify that the data was properly
+loaded.  For example,
+\begin{verbatim}
+> data <- read.csv("mydata.csv", header = TRUE)             # Read the data.
+> dim(data)                    # Displays the dimensions of the data frame
+[1] 16000  8                   #  in rows then columns.   
+> data[1:10,]                  # Display rows 1-10 and all columns.
+> names(data)                  # Check the variable names.
+[1] "V1" "V2" "V3"             # These values indicate that the variables  
+                               #  weren't named, and took default values.
+> names(data) <- c("income", "educate", "year")     # Assign variable names. 
+> summary(data)                # Returning a summary for each variable.  
+\end{verbatim}
+In this case, the {\tt summary()} command will return the maximum,
+minimum, mean, median, first and third quartiles, as well as the
+number of missing values for each variable.
+
+\subsection{Saving Data}\label{s:save}
+
+Use \texttt{save()} to write data or any object to a file in your
+working directory.  For example,
+\begin{verbatim}
+> save(mydata, file = "mydata.RData")     # Saves `mydata' to `mydata.RData'
+                                          #  in your working directory.  
+> save.image()                            # Saves your entire workspace to
+                                          #  the default `.RData' file.
+\end{verbatim}
+R will also prompt you to save your workspace when you use the
+\texttt{q()} command to quit.  When you start R again, it will load
+the previously saved workspace.  Restarting R will not, however, load
+previously used packages.  You must remember to load Zelig at the
+beginning of every R session.
+
+Alternatively, you can recall individually saved objects from .RData
+files using the {\tt load()} command.  For example,
+\begin{verbatim}
+> load("mydata.RData") 
+\end{verbatim}
+loads the objects saved in the {\tt mydata.RData} file.  You may save
+a data frame, a data frame and associated functions, or other R
+objects to file.
+
+\section{Variables}
+
+\subsection{Classes of Variables}
+
+R variables come in several types.  Certain Zelig models require
+dependent variables of a certain class of variable.  (These are
+documented under the manual pages for each model.)  Use {\tt
+  class(variable)} to determine the class of a variable or {\tt
+  class(data\$variable)} for a variable within a data frame.  %$
+
+\subsubsection*{Types of Variables} \label{variable.classes}
+
+For all types of variable (vectors), you may use the {\tt c()} command
+to ``concatenate'' elements into a vector, the {\tt :} operator to
+generate a sequence of integer values, the {\tt seq()} command to
+generate a sequence of non-integer values, or the {\tt rep()} function
+to repeat a value to a specified length.  In addition, you may use the
+{\tt <-} operator to save variables (or any other objects) to the
+workspace.  For example:
+\begin{verbatim}
+> logic <- c(TRUE, FALSE, TRUE, TRUE, TRUE) # Creates `logic' (5 T/F values).
+> var1 <- 10:20                             # All integers between 10 and 20.  
+> var2 <- seq(from = 5, to = 10, by = 0.5)  # Sequence from 5 to 10 by 
+                                            #  intervals of 0.5. 
+> var3 <- rep(NA, length = 20)              # 20 `NA' values.  
+> var4 <- c(rep(1, 15), rep(0, 15))         # 15 `1's followed by 15 `0's.  
+\end{verbatim} 
+For the {\tt seq()} command, you may alternatively specify {\tt
+  length} instead of {\tt by} to create a variable with a specific
+number (denoted by the {\tt length} argument) of evenly spaced
+elements.
+
+\begin{enumerate}
+\item \textbf{Numeric} variables are real numbers and the default
+  variable class for most dataset values.  You can perform any type of
+  math or logical operation on numeric values.  If {\tt var1} and {\tt
+    var2} are numeric variables, we can compute
+\begin{verbatim}
+> var3 <- log(var2) - 2*var1      # Create `var3' using math operations.
+\end{verbatim} 
+  \texttt{Inf} (infinity), \texttt{-Inf} (negative infinity),
+  \texttt{NA} (missing value), and \texttt{NaN} (not a number) are
+  special numeric values on which most math operations will fail.
+  (Logical operations will work, however.)  Use {\tt as.numeric()} to
+  transform variables into numeric variables.  Integers are a special
+  class of numeric variable.
+  
+\item \textbf{Logical} variables contain values of either
+  \texttt{TRUE} or \texttt{FALSE}.  R supports the following logical
+  operators: {\tt ==}, exactly equals; {\tt >}, greater than; {\tt <},
+  less than; {\tt >=}, greater than or equals; {\tt <=}, less than or
+  equals; and {\tt !=}, not equals.  The {\tt =} symbol is \emph{not}
+  a logical operator.  Refer to \Sref{logical} for more detail on
+  logical operators.  If {\tt var1} and {\tt var2} both have $n$
+  observations, commands such as
+\begin{verbatim}
+> var3 <- var1 < var2
+> var3 <- var1 == var2
+\end{verbatim}
+  create $n$ {\tt TRUE}/{\tt FALSE} observations such that the $i$th
+  observation in {\tt var3} evaluates whether the logical statement is
+  true for the $i$th value of {\tt var1} with respect to the $i$th
+  value of {\tt var2}.  Logical variables should usually be converted
+  to integer values prior to analysis; use the {\tt as.integer()}
+  command.
+  
+\item \textbf{Character} variables are sets of text strings.  Note
+  that text strings are always enclosed in quotes to denote that the
+  string is a value, not an object in the workspace or an argument for
+  a function (neither of which take quotes).  Variables of class
+  character are not normally used in data analysis, but used as
+  descriptive fields.  If a character variable is used in a
+  statistical operation, it must first be transformed into a factored
+  variable.
+  
+\item \textbf{Factor} variables may contain values consisting of
+  either integers or character strings.  Use {\tt factor()} or {\tt
+    as.factor()} to convert character or integer variables into factor
+  variables.  Factor variables separate unique values into levels.
+  These levels may either be ordered or unordered.  In practice, this
+  means that including a factor variable among the explanatory
+  variables is equivalent to creating dummy variables for each level.
+  In addition, some models (ordinal logit, ordinal probit, and
+  multinomial logit), require that the dependent variable be a factor
+  variable.
+\end{enumerate}
+
+\subsection{Recoding Variables}
+
+Researchers spend a significant amount of time cleaning and recoding
+data prior to beginning their analyses.  R has several procedures to
+facilitate the process.
+
+\subsubsection*{Extracting, Replacing, and Generating New Variables}
+
+While it is not difficult to recode variables, the process is prone to
+human error.  Thus, we recommend that before altering the data, you
+save your existing data frame using the procedures described
+in~\Sref{s:save}, that you only recode one variable at a time, and
+that you recode the variable outside the data frame and then return it
+to the data frame.
+
+To extract the variable you wish to recode, type:
+\begin{verbatim}
+> var <- data$var1               # Copies `var1' from `data', creating `var'.
+\end{verbatim} %$
+Do \emph{not} sort the extracted variable or delete observations from
+it.  If you do, the $i$th observation in {\tt var} will no longer
+match the $i$th observation in {\tt data}.
+
+To replace the variable or generate a new variable in the data frame,
+type: \label{insert}
+\begin{verbatim}
+> data$var1 <- var                # Replace `var1' in `data' with `var'.  
+> data$new.var <- var             # Generate `new.var' in `data' using `var'. 
+\end{verbatim}
+
+To remove a variable from a data frame (rather than replacing one
+variable with another): 
+\begin{verbatim}
+> data$var1 <- NULL
+\end{verbatim} %$
+
+\subsubsection*{Logical Operators}\label{logical}
+
+R has an intuitive method for recoding variables, which relies on
+logical operators that return statements of {\tt TRUE} and {\tt
+  FALSE}.  A mathematical operator (such as {\tt ==}, {\tt !=}, {\tt
+  >}, {\tt >=} {\tt <}, and {\tt <=}) takes two objects of equal
+dimensions (scalars, vectors of the same length, matrices with the
+same number of rows and columns, or similarly dimensioned arrays) and
+compares every element in the first object to its counterpart in the
+second object.
+
+\begin{itemize}
+\item {\tt ==}: checks that one variable ``exactly equals'' another in
+  a list-wise manner.  For example:
+\begin{verbatim}
+> x <- c(1, 2, 3, 4, 5)                # Creates the object `x'.
+> y <- c(2, 3, 3, 5, 1)                # Creates the object `y'.  
+> x == y                               # Only the 3rd `x' exactly equals
+  [1] FALSE FALSE  TRUE FALSE FALSE    #  its counterpart in `y'.  
+\end{verbatim} 
+(The {\tt =} symbol is \emph{not} a logical operator.)
+
+\item {\tt !=}: checks that one variable does not equal the other in a
+  list-wise manner. Continuing the example:
+\begin{verbatim}
+> x != y
+  [1]  TRUE  TRUE FALSE  TRUE  TRUE
+\end{verbatim}
+  
+\item {\tt >} ({\tt >=}): checks whether each element in the left-hand
+  object is greater than (or equal to) every element in the right-hand
+  object.  Continuing the example from above:
+\begin{verbatim}
+> x > y                                   # Only the 5th `x' is greater
+  [1] FALSE FALSE FALSE FALSE  TRUE       #  than its counterpart in `y'.
+> x >= y                                  # The 3rd `x' is equal to the    
+  [1] FALSE FALSE  TRUE FALSE  TRUE       #  3rd `y' and becomes TRUE.  
+\end{verbatim}
+  
+\item {\tt <} ({\tt <=}): checks whether each element in the left-hand
+  object is less than (or equal to) every object in the right-hand
+  object.  Continuing the example from above:
+\begin{verbatim}
+> x < y                           # The elements 1, 2, and 4 of `x' are 
+[1]  TRUE  TRUE FALSE  TRUE FALSE #  less than their counterparts in `y'. 
+> x <= y                          # The 3rd `x' is equal to the 3rd `y'
+[1]  TRUE  TRUE  TRUE  TRUE FALSE #  and becomes TRUE.  
+\end{verbatim}
+\end{itemize}
+For two vectors of five elements, the mathematical operators compare
+the first element in {\tt x} to the first element in {\tt y}, the
+second to the second and so forth.  Thus, a mathematical comparison of
+{\tt x} and {\tt y} returns a vector of five {\tt TRUE}/{\tt FALSE}
+statements.  Similarly, for two matrices with 3 rows and 20 columns
+each, the mathematical operators will return a $3 \times 20$ matrix of
+logical values.
+
+There are additional logical operators which allow you to combine and
+compare logical statements:
+\begin{itemize}
+\item {\tt \&}: is the logical equivalent of ``and'', and evaluates
+one array of logical statements against another in a list-wise manner,
+returning a {\tt TRUE} only if both are true in the same location.  For example:
+\begin{verbatim}
+> a <- matrix(c(1:12), nrow = 3, ncol = 4)      # Creates a matrix `a'.  
+> a
+     [,1] [,2] [,3] [,4]
+[1,]    1    4    7   10
+[2,]    2    5    8   11
+[3,]    3    6    9   12
+> b <- matrix(c(12:1), nrow = 3, ncol = 4)      # Creates a matrix `b'.  
+> b
+     [,1] [,2] [,3] [,4]
+[1,]   12    9    6    3
+[2,]   11    8    5    2
+[3,]   10    7    4    1
+> v1 <- a > 3                    # Creates the matrix `v1' (T/F values). 
+> v2 <- b > 3                    # Creates the matrix `v2' (T/F values). 
+> v1 & v2                        # Checks if the (i,j) value in `v1' and 
+      [,1] [,2] [,3]  [,4]       #  `v2' are both TRUE.  Because columns 
+[1,] FALSE TRUE TRUE FALSE       #  2-4 of `v1' are TRUE, and columns 1-3
+[2,] FALSE TRUE TRUE FALSE       #  of `var2' are TRUE, columns 2-3 are
+[3,] FALSE TRUE TRUE FALSE       #  TRUE here.  
+> (a > 3) & (b > 3)              # The same, in one step.  
+\end{verbatim}
+  For more complex comparisons, parentheses may be necessary to
+  delimit logical statements.
+  
+\item {\tt |}: is the logical equivalent of ``or'', and evaluates in a
+list-wise manner whether either of the
+  values are {\tt TRUE}.  Continuing the example from above:
+\begin{verbatim}
+> (a < 3) | (b < 3)                # (1,1) and (2,1) in `a' are less
+      [,1]  [,2]  [,3]  [,4]       #  than 3, and (2,4) and (3,4) in
+[1,]  TRUE FALSE FALSE FALSE       #  `b' are less than 3; | returns
+[2,]  TRUE FALSE FALSE  TRUE       #  a matrix with `TRUE' in (1,1),
+[3,] FALSE FALSE FALSE  TRUE       #  (2,1), (2,4), and (3,4).  
+\end{verbatim}
+\end{itemize}
+The {\tt \&\&} (if and only if) and {\tt ||} (either or) operators are
+used to control the command flow within functions.  The {\tt \&\&}
+operator returns a {\tt TRUE} only if every element in the comparison
+statement is true; the {\tt ||} operator returns a {\tt TRUE} if any
+of the elements are true.  Unlike the {\tt \&} and {\tt |} operators,
+which return arrays of logical values, the {\tt \&\&} and {\tt ||}
+operators return only one logical statement irrespective of the
+dimensions of the objects under consideration.  Hence, {\tt \&\&} and
+{\tt ||} are logical operators which are \emph{not} appropriate for
+recoding variables.
+
+\subsubsection*{Coding and Recoding Variables}
+
+R uses vectors of logical statements to indicate how a variable should
+be coded or recoded.  For example, to create a new variable {\tt var3}
+equal to 1 if {\tt var1} $<$ {\tt var2} and 0 otherwise:
+\begin{verbatim}
+> var3 <- var1 < var2               # Creates a vector of n T/F observations.
+> var3 <- as.integer(var3)          # Replaces the T/F values in `var3' with 
+                                    #  1's for TRUE and 0's for FALSE.  
+> var3 <- as.integer(var1 < var2)   # Combine the two steps above into one.
+\end{verbatim}
+
+In addition to generating a vector of dummy variables, you can also
+refer to specific values using logical operators defined in
+\Sref{logical}.  For example:
+\begin{verbatim}
+> v1 <- var1 == 5                     # Creates a vector of T/F statements.
+> var1[v1] <- 4                       # For every TRUE in `v1', replaces the 
+                                      #  value in `var1' with a 4.  
+> var1[var1 == 5] <- 4                # The same, in one step.  
+\end{verbatim}
+The index (inside the square brackets) can be created with reference
+to other variables.  For example,
+\begin{verbatim}
+> var1[var2 == var3] <- 1  
+\end{verbatim}
+replaces the $i$th value in {\tt var1} with a 1 when the $i$th value
+in {\tt var2} equals the $i$th value in {\tt var3}.  If you use {\tt
+  =} in place of {\tt ==}, however, you will replace all the values in
+{\tt var1} with 1's because {\tt =} is another way to assign
+variables.  Thus, the statement {\tt var2 = var3} is of course true.
+
+Finally, you may also replace any (character, numerical, or logical)
+values with special values (most commonly, {\tt NA}).
+\begin{verbatim}
+> var1[var1 == "don't know"] <- NA   # Replaces all "don't know"'s with NA's.
+\end{verbatim}
+
+After recoding the {\tt var1} replace the old {\tt data\$var1} with
+the recoded {\tt var1}: {\tt data\$var1 <- var1}.  You may combine the
+recoding and replacement procedures into one step.  For example:
+\begin{verbatim}
+> data$var1[data$var1 =< 0] <- -1
+\end{verbatim}
+
+Alternatively, rather than recoding just specific values in variables,
+you may calculate new variables from existing variables.  For example,
+\begin{verbatim}
+> var3 <- var1 + 2 * var2   
+> var3 <- log(var1)         
+\end{verbatim}
+After generating the new variables, use the assignment mechanism {\tt
+  <-} to insert the new variable into the data frame.
+
+In addition to generating vectors of dummy variables, you may
+transform a vector into a matrix of dummy indicator variables.  For
+example, see \Sref{dummy} to transform a vector of $k$ unique values
+(with $n$ observations in the complete vector) into a $n \times k$
+matrix.
+
+\subsubsection*{Missing Data} 
+
+To deal with missing values in some of your variables:
+\begin{enumerate}
+\item You may generate multiply imputed datasets using
+  \hlink{Amelia}{http://gking.harvard.edu/stats.shtml\#amelia} (or
+  other programs).
+\item You may omit missing values.  Zelig models automatically apply
+  list-wise deletion, so no action is required to run a model.  To
+  obtain the total number of observations or produce other summary
+  statistics using the analytic dataset, you may manually omit
+  incomplete observations.  To do so, first create a data frame
+  containing only the variables in your analysis.  For example:
+\begin{verbatim}
+> new.data <- cbind(data$dep.var, data$var1, data$var2, data$var3)
+\end{verbatim}
+  The {\tt cbind()} command ``column binds'' variables into a data
+  frame.  (A similar command {\tt rbind()} ``row binds'' observations
+  with the same number of variables into a data frame.)  To omit
+  missing values from this new data frame:
+\begin{verbatim}
+> new.data <- na.omit(new.data)
+\end{verbatim}
+  If you perform {\tt na.omit()} on the full data frame, you risk
+  deleting observations that are fully observed in your experimental
+  variables, but missing values in other variables.  Creating a new
+  data frame containing only your experimental variables usually
+  increases the number of observations retained after {\tt na.omit()}.
+\end{enumerate}
+
+%%% Local Variables: 
+%%% mode: latex
+%%% TeX-master: "zelig"
+%%% End: 
diff --git a/inst/doc/upquote.sty b/inst/doc/upquote.sty
new file mode 100644
index 0000000..67b4703
--- /dev/null
+++ b/inst/doc/upquote.sty
@@ -0,0 +1,76 @@
+\NeedsTeXFormat{LaTeX2e}[1995/12/01]
+\ProvidesPackage{upquote}[2003/08/11 v1.1 Covington's upright-quote
+                          modification to verbatim and verb]
+
+%% Copyright 2000 Michael A. Covington.
+%% Copyright 2003 Michael A. Covington, Frank Mittelbach.
+%%
+%% It may be distributed and/or modified under the
+%% conditions of the LaTeX Project Public License, either version 1.2
+%% of this license or (at your option) any later version.
+%% The latest version of this license is in
+%%    http://www.latex-project.org/lppl.txt
+%% and version 1.2 or later is part of all distributions of LaTeX
+%% version 1999/12/01 or later.
+%%
+
+% Problem: Computer Modern Typewriter is the best font for program listings,
+%           *but* it prints ` ' as bent opening and closing single quotes.
+%           Other fonts, and most programming languages, print ` as a grave
+%           accent and ' upright; ' is used both to open and to close quoted
+%           strings.
+
+%           See also M. Covington, "Computer Languages in Type," Journal of
+%           Scholarly Publishing 26.1:34-41 (1994).
+
+% Solution: This package switches the typewriter font to Computer Modern
+%           Typewriter (regardless of other fonts in use, so long as this
+%           package is called afterward) and modifies the behavior of
+%           verbatim, verbatim*, verb, and verb* to print ` ' the desired way.
+%           It does not affect \tt, \texttt, etc.
+
+% Author:   Michael A. Covington
+%           Artificial Intelligence Center
+%           The University of Georgia
+%           http://www.ai.uga.edu/~mc
+%
+%           and
+%
+%           Covington Innovations (Consulting and Typesetting)
+%           http://www.CovingtonInnovations.com
+
+% The modification is done by adding instructions to \@noligs,
+% which is called by verbatim and verb in order to turn the
+% characters ` < > , ' - into active characters that merely
+% print themselves rather than activating ligatures.
+%
+% What is added is code to redefine ` as grave and ' as upright single quote.
+%
+
+% Bug fix, 2000/12/11: previously, '0 (or ' and any digit) would print as a
+% nonexistent character.  (The reason: \char13 or \char18 was combining with
+% the digit to make \char130, etc.)  Fixed by adding curly brackets.
+
+
+
+%% Rewritten by FMi 2003/06/19
+%
+%
+% Use textcomp official chars so that change works in various
+% encodings
+%
+% Extend \@noligs (this also works with the latest listings package
+% that recognizes that the package was loaded)
+
+\RequirePackage{textcomp}
+
+\begingroup
+\catcode`'=\active               
+\catcode``=\active               
+\g at addto@macro\@noligs
+   {\let`\textasciigrave
+    \let'\textquotesingle}
+\endgroup
+
+
+\endinput
diff --git a/inst/doc/zelig.aux b/inst/doc/zelig.aux
new file mode 100644
index 0000000..16f760a
--- /dev/null
+++ b/inst/doc/zelig.aux
@@ -0,0 +1,364 @@
+\relax 
+\ifx\hyper at anchor\@undefined
+\global \let \oldcontentsline\contentsline
+\gdef \contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
+\global \let \oldnewlabel\newlabel
+\gdef \newlabel#1#2{\newlabelxx{#1}#2}
+\gdef \newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
+\AtEndDocument{\let \contentsline\oldcontentsline
+\let \newlabel\oldnewlabel}
+\else
+\global \let \hyper at last\relax 
+\fi
+
+\@input{acknowledgments.aux}
+\citation{KinTomWit00}
+\citation{KinHonJos01}
+\citation{King95}
+\@writefile{toc}{\contentsline {chapter}{\numberline {1}Introduction}{6}{chapter.1}}
+\@writefile{lof}{\addvspace {10\p@ }}
+\@writefile{lot}{\addvspace {10\p@ }}
+\@writefile{toc}{\contentsline {section}{\numberline {1.1}What Zelig and R Do}{6}{section.1.1}}
+\@writefile{toc}{\contentsline {section}{\numberline {1.2}Getting Help}{7}{section.1.2}}
+\newlabel{Rhelp}{{1.2}{8}{Getting Help\relax }{section.1.2}{}}
+\@writefile{toc}{\contentsline {part}{I\hspace  {1em}User's Guide}{9}{part.1}}
+\@writefile{toc}{\contentsline {chapter}{\numberline {2}Installation}{10}{chapter.2}}
+\@writefile{lof}{\addvspace {10\p@ }}
+\@writefile{lot}{\addvspace {10\p@ }}
+\@writefile{toc}{\contentsline {subsubsection}{If You Know R}{10}{section*.3}}
+\@writefile{toc}{\contentsline {subsubsection}{If You Are New to R}{10}{section*.4}}
+\@writefile{toc}{\contentsline {section}{\numberline {2.1}Windows}{11}{section.2.1}}
+\newlabel{ss:win}{{2.1}{11}{Windows\relax }{section.2.1}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Installing R}{11}{section*.5}}
+\@writefile{toc}{\contentsline {subsubsection}{Installing Zelig}{11}{section*.6}}
+\newlabel{manual.windows}{{3}{11}{Installing Zelig\relax }{Item.3}{}}
+\newlabel{win.zelig}{{3a}{11}{Installing Zelig\relax }{Item.4}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Updating Zelig}{12}{section*.7}}
+\@writefile{toc}{\contentsline {section}{\numberline {2.2}MacOS X}{12}{section.2.2}}
+\newlabel{ss:osx}{{2.2}{12}{MacOS X\relax }{section.2.2}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Installing R}{12}{section*.8}}
+\@writefile{toc}{\contentsline {subsubsection}{Installing Zelig}{12}{section*.9}}
+\newlabel{osx.manual}{{2.2}{12}{Installing Zelig\relax }{section*.9}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Updating Zelig}{14}{section*.10}}
+\@writefile{toc}{\contentsline {section}{\numberline {2.3}UNIX and Linux}{15}{section.2.3}}
+\newlabel{ss:unix}{{2.3}{15}{UNIX and Linux\relax }{section.2.3}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Installing R}{15}{section*.11}}
+\@writefile{toc}{\contentsline {subsubsection}{Installing Zelig}{15}{section*.12}}
+\newlabel{sss:unix.library}{{2.3}{15}{Installing Zelig\relax }{section*.12}{}}
+\newlabel{unix.zelig}{{2.3}{15}{Installing Zelig\relax }{section*.12}{}}
+\newlabel{unix.manual}{{2}{16}{Installing Zelig\relax }{Item.35}{}}
+\@writefile{toc}{\contentsline {subsubsection}{Updating Zelig}{16}{section*.13}}
+\newlabel{sss:unix.updating}{{2.3}{16}{Updating Zelig\relax }{section*.13}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {2.4}Version Compatability}{17}{section.2.4}}
+\@writefile{toc}{\contentsline {chapter}{\numberline {3}Data Analysis Commands}{18}{chapter.3}}
+\@writefile{lof}{\addvspace {10\p@ }}
+\@writefile{lot}{\addvspace {10\p@ }}
+\newlabel{c:R}{{3}{18}{Data Analysis Commands\relax }{chapter.3}{}}
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+\bibcite{AdoKinHer03}{{1}{2003}{{Adolph et~al.}}{{Adolph, Gary~King, and Shotts}}}
+\bibcite{Andrews91}{{2}{1991}{{Andrews}}{{}}}
+\bibcite{BreFriOls84}{{3}{1984}{{Breiman et~al.}}{{Breiman, Friedman, Olshen, and Stone}}}
+\bibcite{BroDav91}{{4}{1991}{{Brockwell and Davis}}{{}}}
+\bibcite{DiaSek05}{{5}{2005}{{Diamond and Sekhon}}{{}}}
+\bibcite{Enders04}{{6}{2004}{{Enders}}{{}}}
+\bibcite{Friendly00}{{7}{2000}{{Friendly}}{{}}}
+\bibcite{GelKin94a}{{8}{1994}{{Gelman and King}}{{}}}
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+\bibcite{HoImaKin05b}{{11}{2005}{{Ho et~al.}}{{Ho, Imai, King, and Stuart}}}
+\bibcite{HoImaKin06}{{12}{2006}{{Ho et~al.}}{{Ho, Imai, King, and Stuart}}}
+\bibcite{Huber81}{{13}{1981}{{Huber}}{{}}}
+\bibcite{Imai05}{{14}{2005}{{Imai}}{{}}}
+\bibcite{KatKin99}{{15}{1999}{{Katz and King}}{{}}}
+\bibcite{King89}{{16}{1989}{{King}}{{}}}
+\bibcite{King95}{{17}{1995}{{King}}{{}}}
+\bibcite{King97}{{18}{1997}{{King}}{{}}}
+\bibcite{KinAltBur90}{{19}{1990}{{King et~al.}}{{King, Alt, Burns, and Laver}}}
+\bibcite{KinHonJos01}{{20}{2001}{{King et~al.}}{{King, Honaker, Joseph, and Scheve}}}
+\bibcite{KinMurSal04}{{21}{2004}{{King et~al.}}{{King, Murray, Salomon, and Tandon}}}
+\bibcite{KinTomWit00}{{22}{2000}{{King et~al.}}{{King, Tomz, and Wittenberg}}}
+\bibcite{KinZen01b}{{23}{2001{a}}{{King and Zeng}}{{}}}
+\bibcite{KinZen01}{{24}{2001{b}}{{King and Zeng}}{{}}}
+\bibcite{KinZen02b}{{25}{2002{a}}{{King and Zeng}}{{}}}
+\bibcite{KinZen02}{{26}{2002{b}}{{King and Zeng}}{{}}}
+\bibcite{KinZen06a}{{27}{2006}{{King and Zeng}}{{}}}
+\bibcite{LumHea99}{{28}{1999}{{Lumley and Heagerty}}{{}}}
+\bibcite{MarQui05}{{29}{2005}{{Martin and Quinn}}{{}}}
+\bibcite{Martin92}{{30}{1992}{{Martin}}{{}}}
+\bibcite{McCNel89}{{31}{1989}{{McCullagh and Nelder}}{{}}}
+\bibcite{PluBesCowVin05}{{32}{2005}{{Plummer et~al.}}{{Plummer, Best, Cowles, and Vines}}}
+\bibcite{Qui04}{{33}{2004}{{Quinn}}{{}}}
+\bibcite{Ripley96}{{34}{1996}{{Ripley}}{{}}}
+\bibcite{RosJiaKin01}{{35}{2001}{{Rosen et~al.}}{{Rosen, Jiang, King, and Tanner}}}
+\bibcite{SchSla01}{{36}{2001}{{Scheve and Slaughter}}{{}}}
+\bibcite{StoKinZen06}{{37}{2006}{{Stoll et~al.}}{{Stoll, King, and Zeng}}}
+\bibcite{VenRip02}{{38}{2002}{{Venables and Ripley}}{{}}}
+\bibcite{White80}{{39}{1980}{{White}}{{}}}
+\bibcite{YeeHas03}{{40}{2003}{{Yee and Hastie}}{{}}}
+\bibcite{Zeileis04}{{41}{2004}{{Zeileis}}{{}}}
diff --git a/inst/doc/zelig.bbl b/inst/doc/zelig.bbl
new file mode 100644
index 0000000..0cd91aa
--- /dev/null
+++ b/inst/doc/zelig.bbl
@@ -0,0 +1,206 @@
+\begin{thebibliography}{41}
+\newcommand{\enquote}[1]{``#1''}
+\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi
+
+\bibitem[{Adolph et~al.(2003)Adolph, Gary~King, and Shotts}]{AdoKinHer03}
+Adolph, C., Gary~King, w. M. C.~H., and Shotts, K.~W. (2003), \enquote{A
+  Consensus on Second Stage Analyses in Ecological Inference Models,}
+  \textit{Political Analysis}, 11, 86--94,
+  http://gking.harvard.edu/files/abs/akhs-abs.shtml.
+
+\bibitem[{Andrews(1991)}]{Andrews91}
+Andrews, D.~W. (1991), \enquote{Heteroskedasticity and Autocorrelation
+  Consistent Covariance Matrix Estimation,} \textit{Econometrica}, 59,
+  817--858.
+
+\bibitem[{Breiman et~al.(1984)Breiman, Friedman, Olshen, and
+  Stone}]{BreFriOls84}
+Breiman, L., Friedman, J.~H., Olshen, R.~A., and Stone, C.~J. (1984),
+  \textit{Classification and Regression Trees}, New York, New York: Chapman \&
+  Hall.
+
+\bibitem[{Brockwell and Davis(1991)}]{BroDav91}
+Brockwell, P.~J. and Davis, R.~A. (1991), \textit{Time Series: Theory and
+  Methods}, Springer-Verlag, 2nd ed.
+
+\bibitem[{Diamond and Sekhon(2005)}]{DiaSek05}
+Diamond, A. and Sekhon, J. (2005), \enquote{Genetic Matching for Estimating
+  Causal Effects: A New Method of Achieving Balance in Observational Studies,}
+  http://jsekhon.fas.harvard.edu/.
+
+\bibitem[{Enders(2004)}]{Enders04}
+Enders, W. (2004), \textit{Applied Econometric Time Series}, Wiley, 2nd ed.
+
+\bibitem[{Friendly(2000)}]{Friendly00}
+Friendly, M. (2000), \textit{Visualizing Categorical Data}, SAS Institute.
+
+\bibitem[{Gelman and King(1994)}]{GelKin94a}
+Gelman, A. and King, G. (1994), \enquote{A Unified Method of Evaluating
+  Electoral Systems and Redistricting Plans,} \textit{American Journal of
+  Political Science}, 38, 514--554,
+  http://gking.harvard.edu/files/abs/writeit-abs.shtml.
+
+\bibitem[{Hansen(2004)}]{Hansen04}
+Hansen, B.~B. (2004), \enquote{Full Matching in an Observational Study of
+  Coaching for the {SAT},} \textit{Journal of the American Statistical
+  Association}, 99, 609--618.
+
+\bibitem[{Hastie and Tibshirani(1990)}]{HasTib90}
+Hastie, T.~J. and Tibshirani, R. (1990), \textit{Generalized Additive Models},
+  London: Chapman Hall.
+
+\bibitem[{Ho et~al.(2005)Ho, Imai, King, and Stuart}]{HoImaKin05b}
+Ho, D., Imai, K., King, G., and Stuart, E. (2005), \enquote{Matching as
+  Nonparametric Preprocessing for Parametric Causal Inference,}
+  Http://gking.harvard.edu/matchit/.
+
+\bibitem[{Ho et~al.(2006)Ho, Imai, King, and Stuart}]{HoImaKin06}
+--- (2006), \enquote{Matching as Nonparametric Preprocessing for Parametric
+  Causal Inference,} Http://gking.harvard.edu/files/abs/matchp-abs.shtml.
+
+\bibitem[{Huber(1981)}]{Huber81}
+Huber, P.~J. (1981), \textit{Robust Statistics}, Wiley.
+
+\bibitem[{Imai(2005)}]{Imai05}
+Imai, K. (2005), \enquote{Do Get-Out-The-Vote Calls Reduce Turnout? The
+  Importance of Statistical Methods for Field Experiments,} \textit{American
+  Political Science Review}, 99, 283--300.
+
+\bibitem[{Katz and King(1999)}]{KatKin99}
+Katz, J. and King, G. (1999), \enquote{A Statistical Model for Multiparty
+  Electoral Data,} \textit{American Political Science Review}, 93, 15--32,
+  http://gking.harvard.edu/files/abs/multiparty-abs.shtml.
+
+\bibitem[{King(1989)}]{King89}
+King, G. (1989), \textit{Unifying Political Methodology: The Likelihood Theory
+  of Statistical Inference}, Michigan University Press.
+
+\bibitem[{King(1995)}]{King95}
+--- (1995), \enquote{Replication, Replication,} \textit{PS: Political Science
+  and Politics}, 28, 443--499,
+  http://gking.harvard.edu/files/abs/replication-abs.shtml.
+
+\bibitem[{King(1997)}]{King97}
+--- (1997), \textit{A Solution to the Ecological Inference Problem:
+  Reconstructing Individual Behavior from Aggregate Data}, Princeton: Princeton
+  University Press.
+
+\bibitem[{King et~al.(1990)King, Alt, Burns, and Laver}]{KinAltBur90}
+King, G., Alt, J., Burns, N., and Laver, M. (1990), \enquote{A Unified Model of
+  Cabinet Dissolution in Parliamentary Democracies,} \textit{American Journal
+  of Political Science}, 34, 846--871,
+  http://gking.harvard.edu/files/abs/coal-abs.shtml.
+
+\bibitem[{King et~al.(2001)King, Honaker, Joseph, and Scheve}]{KinHonJos01}
+King, G., Honaker, J., Joseph, A., and Scheve, K. (2001), \enquote{Analyzing
+  Incomplete Political Science Data: An Alternative Algorithm for Multiple
+  Imputation,} \textit{American Political Science Review}, 95, 49--69,
+  http://gking.harvard.edu/files/abs/evil-abs.shtml.
+
+\bibitem[{King et~al.(2004)King, Murray, Salomon, and Tandon}]{KinMurSal04}
+King, G., Murray, C.~J., Salomon, J.~A., and Tandon, A. (2004),
+  \enquote{Enhancing the Validity and Cross-cultural Comparability of
+  Measurement in Survey Research,} \textit{American Political Science Review},
+  94, 191--205, http://gking.harvard.edu/files/abs/vign-abs.shtml.
+
+\bibitem[{King et~al.(2000)King, Tomz, and Wittenberg}]{KinTomWit00}
+King, G., Tomz, M., and Wittenberg, J. (2000), \enquote{Making the Most of
+  Statistical Analyses: Improving Interpretation and Presentation,}
+  \textit{American Journal of Political Science}, 44, 341--355,
+  http://gking.harvard.edu/files/abs/making-abs.shtml.
+
+\bibitem[{King and Zeng(2001{\natexlab{a}})}]{KinZen01b}
+King, G. and Zeng, L. (2001{\natexlab{a}}), \enquote{Explaining Rare Events in
+  International Relations,} \textit{International Organization}, 55, 693--715,
+  http://gking.harvard.edu/files/abs/baby0s-abs.shtml.
+
+\bibitem[{King and Zeng(2001{\natexlab{b}})}]{KinZen01}
+--- (2001{\natexlab{b}}), \enquote{Logistic Regression in Rare Events Data,}
+  \textit{Political Analysis}, 9, 137--163,
+  http://gking.harvard.edu/files/abs/0s-abs.shtml.
+
+\bibitem[{King and Zeng(2002{\natexlab{a}})}]{KinZen02b}
+--- (2002{\natexlab{a}}), \enquote{Estimating Risk and Rate Levels, Ratios, and
+  Differences in Case-Control Studies,} \textit{Statistics in Medicine}, 21,
+  1409--1427, http://gking.harvard.edu/files/abs/1s-abs.shtml.
+
+\bibitem[{King and Zeng(2002{\natexlab{b}})}]{KinZen02}
+--- (2002{\natexlab{b}}), \enquote{Improving Forecasts of State Failure,}
+  \textit{World Politics}, 53, 623--658,
+  http://gking.harvard.edu/files/abs/civil-abs.shtml.
+
+\bibitem[{King and Zeng(2006)}]{KinZen06a}
+--- (2006), \enquote{The Dangers of Extreme Counterfactuals,} \textit{Political
+  Analysis}, 14, 131--159,
+  http://gking.harvard.edu/files/abs/counterft-abs.shtml.
+
+\bibitem[{Lumley and Heagerty(1999)}]{LumHea99}
+Lumley, T. and Heagerty, P. (1999), \enquote{Weighted Empirical Adaptive
+  Variance Estimators for Correlated Data Regression,} \textit{jrssb}, 61,
+  459--477.
+
+\bibitem[{Martin and Quinn(2005)}]{MarQui05}
+Martin, A.~D. and Quinn, K.~M. (2005), \textit{MCMCpack: Markov chain Monte
+  Carlo (MCMC) Package}.
+
+\bibitem[{Martin(1992)}]{Martin92}
+Martin, L. (1992), \textit{Coercive Cooperation: Explaining Multilateral
+  Economic Sanctions}, Princeton University Press, please inquire with Lisa
+  Martin before publishing results from these data, as this dataset includes
+  errors that have since been corrected.
+
+\bibitem[{McCullagh and Nelder(1989)}]{McCNel89}
+McCullagh, P. and Nelder, J.~A. (1989), \textit{Generalized Linear Models},
+  no.~37 in Monograph on Statistics and Applied Probability, Chapman \& Hall,
+  2nd ed.
+
+\bibitem[{Plummer et~al.(2005)Plummer, Best, Cowles, and
+  Vines}]{PluBesCowVin05}
+Plummer, M., Best, N., Cowles, K., and Vines, K. (2005), \textit{coda: Output
+  analysis and diagnostics for MCMC}.
+
+\bibitem[{Quinn(2004)}]{Qui04}
+Quinn, K. (2004), \enquote{Ecological Inference in the Presence of Temporal
+  Dependence,} in \textit{Ecological Inference: New Methodological Strategies},
+  eds. King, G., Rosen, O., and Tanner, M.~A., New York: Cambridge University
+  Press.
+
+\bibitem[{Ripley(1996)}]{Ripley96}
+Ripley, B. (1996), \textit{Pattern Recognition and Neural Networks}, Cambridge
+  Univeristy Press.
+
+\bibitem[{Rosen et~al.(2001)Rosen, Jiang, King, and Tanner}]{RosJiaKin01}
+Rosen, O., Jiang, W., King, G., and Tanner, M.~A. (2001), \enquote{Bayesian and
+  Frequentist Inference for Ecological Inference: The $R \times C$ Case,}
+  \textit{Statistica Neerlandica}, 55, 134--156,
+  http://gking.harvard.edu/files/abs/rosen-abs.shtml.
+
+\bibitem[{Scheve and Slaughter(2001)}]{SchSla01}
+Scheve, K. and Slaughter, M. (2001), \enquote{Labor Market Competition and
+  Individual Preferences over Immigration Policy,} \textit{Review of Economics
+  and Statistics}, 83, 133--145, sample data include only the first five of ten
+  multiply imputed data sets.
+
+\bibitem[{Stoll et~al.(2006)Stoll, King, and Zeng}]{StoKinZen06}
+Stoll, H., King, G., and Zeng, L. (2006), \enquote{WhatIf: Software for
+  Evaluating Counterfactuals,} \textit{Journal of Statistical Software}, 15,
+  http://gking.harvard.edu/whatif/.
+
+\bibitem[{Venables and Ripley(2002)}]{VenRip02}
+Venables, W.~N. and Ripley, B.~D. (2002), \textit{Modern Applied Statistics
+  with S}, Springer-Verlag, 4th ed.
+
+\bibitem[{White(1980)}]{White80}
+White, H. (1980), \enquote{A Heteroskedasticity-Consistent Covariance Matrix
+  Estimator and a Direct Test for Heteroskedasticity,} \textit{Econometrica},
+  48, 817--838.
+
+\bibitem[{Yee and Hastie(2003)}]{YeeHas03}
+Yee, T.~W. and Hastie, T.~J. (2003), \enquote{Reduced-rank vector generalized
+  linear models,} \textit{Statistical Modelling}, 3, 15--41.
+
+\bibitem[{Zeileis(2004)}]{Zeileis04}
+Zeileis, A. (2004), \enquote{Econometric Computing with HC and HAC Covariance
+  Matrix Estimators,} \textit{Journal of Statistical Software}, 11, 1--17.
+
+\end{thebibliography}
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+)
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+\c at section@level=\count134
+)
+LaTeX Info: Redefining \ref on input line 64.
+LaTeX Info: Redefining \pageref on input line 64.
+ (./zelig.out) (./zelig.out)
+\openout3 = `zelig.out'.
+
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+)
+Underfull \hbox (badness 1412) in paragraph at lines 66--66
+[][]/cmr10 at 10.95pt/David Flo-rence Pro-fes-sor of Gov-ern-ment, Har-vard Uni-ve
+r-sity (In-sti-tute for Quan-ti-ta-
+ []
+
+
+Underfull \hbox (badness 1533) in paragraph at lines 66--66
+/cmr10 at 10.95pt/tive So-cial Sci-ences, 1737 Cam-bridge Street, Har-vard Uni-ver
+-sity, Cam-bridge MA 02138;
+ []
+
+[1
+
+
+{/usr/share/texmf/dvips/config/pdftex.map}] (./zelig.toc
+! pdfTeX warning (ext4): destination with the same identifier (name{page.1}) ha
+s been already used, duplicate ignored
+<to be read again> 
+                   \penalty 
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+                                                   [1
+
+] [2] [3])
+\tf at toc=\write7
+\openout7 = `zelig.toc'.
+
+ (./zelig.bbl)
+[4]
+\openout2 = `acknowledgments.aux'.
+
+ (./acknowledgments.tex) [5
+
+] (./intro.tex
+Chapter 1.
+[6
+
+
+] [7]) [8] [9
+
+] (./install.tex
+Chapter 2.
+
+Overfull \hbox (8.01222pt too wide) in paragraph at lines 46--54
+/cmr12/down-load the lat-est []in-staller for Win-dows[] at [][]$/cmtt12/http :
+ / / cran . us . r-[]project . org / bin / windows /
+ []
+
+[10
+
+] [11] [12] [13]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 223.
+
+[14] [15]
+Overfull \hbox (5.36957pt too wide) in paragraph at lines 380--380
+ []/cmtt12/% rm Zelig_XX.tar.gz VGAM_XX.tar.gz zoo_XX.tar.gz sandwich_XX.tar.gz
+[] 
+ []
+
+[16]) (./syntax.tex [17]
+Chapter 3.
+[18
+
+]
+LaTeX Font Info:    Try loading font information for TS1+cmtt on input line 82.
+
+ (/usr/share/texmf/tex/latex/base/ts1cmtt.fd
+File: ts1cmtt.fd 1999/05/25 v2.5h Standard LaTeX font definitions
+) [19]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 92.
+
+[20]
+Overfull \hbox (18.11003pt too wide) in paragraph at lines 159--161
+[]/cmr12/If your dataset is in a /cmbx12/tab- or space-delimited .txt file/cmr1
+2/, use /cmtt12/read.table("mydata.txt") 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 169--169
+ []/cmtt12/> library(foreign)                           # Load the foreign pack
+age. 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 171--171
+ []/cmtt12/> spss.data <- read.spss("mydata.sav", to.data.frame = TRUE) # For S
+PSS.[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 182--182
+ []/cmtt12/> library(Zelig)                              # Loads the Zelig libr
+ary. 
+ []
+
+[21] [22]
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 282--282
+[]/cmtt12/> logic <- c(TRUE, FALSE, TRUE, TRUE, TRUE) # Creates `logic' (5 T/F 
+values).[] 
+ []
+
+
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 283--283
+[]/cmtt12/> var1 <- 10:20                             # All integers between 10
+ and 20.[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 300--300
+ []/cmtt12/> var3 <- log(var2) - 2*var1      # Create `var3' using math operati
+ons.[] 
+ []
+
+[23]
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 366--366
+[]/cmtt12/> var <- data$var1               # Copies `var1' from `data', creatin
+g `var'.[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 367.
+
+[24]
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 376--376
+[]/cmtt12/> data$new.var <- var             # Generate `new.var' in `data' usin
+g `var'.[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 383.
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 402--402
+ []/cmtt12/> x == y                               # Only the 3rd `x' exactly eq
+uals[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 419--419
+ []  /cmtt12/[1] FALSE FALSE FALSE FALSE  TRUE       #  than its counterpart in
+ `y'.[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 429--429
+ []/cmtt12/[1]  TRUE  TRUE FALSE  TRUE FALSE #  less than their counterparts in
+ `y'.[] 
+ []
+
+[25]
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 461--461
+ []/cmtt12/> v1 <- a > 3                    # Creates the matrix `v1' (T/F valu
+es).[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 462--462
+ []/cmtt12/> v2 <- b > 3                    # Creates the matrix `v2' (T/F valu
+es).[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 463--463
+ []/cmtt12/> v1 & v2                        # Checks if the (i,j) value in `v1'
+ and[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 464--464
+ []      /cmtt12/[,1] [,2] [,3]  [,4]       #  `v2' are both TRUE.  Because col
+umns[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 465--465
+ []/cmtt12/[1,] FALSE TRUE TRUE FALSE       #  2-4 of `v1' are TRUE, and column
+s 1-3[] 
+ []
+
+[26]
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 501--501
+[]/cmtt12/> var3 <- var1 < var2               # Creates a vector of n T/F obser
+vations.[] 
+ []
+
+
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 530--530
+[]/cmtt12/> var1[var1 == "don't know"] <- NA   # Replaces all "don't know"'s wi
+th NA's.[] 
+ []
+
+[27]) (./Zcommands.tex [28]
+Chapter 4.
+<xymatrix 3x5 334> [29
+
+]
+
+Package natbib Warning: Citation `KinZen06b' on page 30 undefined on input line
+ 50.
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 90--90
+ []/cmtt12/> z.out <- zelig(vote ~ race + educate, model = "logit", data = turn
+out)[] 
+ []
+
+[30]
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 94--94
+ []/cmtt12/> summary(s.out)                                     # Numerical sum
+mary.[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 95--95
+ []/cmtt12/> plot(s.out)                                        # Graphical sum
+mary.[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 101--101
+ []/cmtt12/> z.out <- zelig(vote ~ race + educate, model = "logit", data = turn
+out)[] 
+ []
+
+[31] [32]
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 211--213
+[]/cmtt12/x.out <- setx(z.out, fn = list(numeric = mean, ordered = median, othe
+rs =
+ []
+
+
+Package natbib Warning: Citation `HoImaKin05' on page 33 undefined on input lin
+e 240.
+
+[33]
+Overfull \hbox (5.36957pt too wide) in paragraph at lines 279--279
+ []/cmtt12/> z.out <- zelig(vote ~ age + race, model = "logit", data = turnout)
+[] 
+ []
+
+[34]
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 361--363
+[]/cmtt12/s.out <- sim(z.out, x = x.out, x1 = NULL, num = c(1000, 100), bootstr
+ap =
+ []
+
+
+Overfull \hbox (1.11974pt too wide) in paragraph at lines 388--399
+[]/cmr12/Zelig sim-u-lates pa-ram-e-ters from clas-si-cal /cmti12/max-i-mum lik
+e-li-hood /cmr12/mod-els us-ing asymp-
+ []
+
+[35] [36]
+Overfull \hbox (10.42874pt too wide) in paragraph at lines 477--483
+[]/cmtt12/"normal"/cmr12/: The /cmti12/Nor-mal /cmr12/(see Sec-tion []12.21[][]
+) model com-putes the maximum-likelihood
+ []
+
+[37]
+Overfull \hbox (2.60545pt too wide) in paragraph at lines 524--528
+[]/cmtt12/"blogit"/cmr12/: The /cmti12/bi-vari-ate lo-gis-tic /cmr12/model (see
+ Sec-tion []12.2[][]) mod-els $[](/cmmi12/Y[] /cmr12/= /cmmi12/y[]; Y[] /cmr12/
+=
+ []
+
+
+Overfull \hbox (2.93037pt too wide) in paragraph at lines 528--531
+[]/cmtt12/"bprobit"/cmr12/: The /cmti12/bi-vari-ate pro-bit /cmr12/model (see S
+ec-tion []12.3[][]) mod-els $[](/cmmi12/Y[] /cmr12/= /cmmi12/y[]; Y[] /cmr12/=
+ []
+
+
+Overfull \hbox (5.26831pt too wide) in paragraph at lines 549--552
+[]/cmtt12/"oprobit"/cmr12/: The /cmti12/or-di-nal pro-bit /cmr12/dis-tri-bu-tio
+n (see Sec-tion []12.24[][]) spec-i-fies the stochas-
+ []
+
+[38] [39]
+Overfull \hbox (5.66435pt too wide) in paragraph at lines 655--658
+[]/cmtt12/ei.RxC/cmr12/: The $/cmmi12/R /cmsy10 at 12.0pt/^^B /cmmi12/C$ /cmcsc10@
+12.0pt/ei /cmr12/model (see Sec-tion []12.6[][]) es-ti-mates a hi-er-ar-chi-cal
+ Multinomial-
+ []
+
+[40] [41]
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 763--763
+[]/cmtt12/# Check that the expected values are identical.  You may do this for 
+each qi.[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 765.
+
+) (./graphs.tex [42]
+Chapter 5.
+[43
+
+] [44]
+Overfull \hbox (6.32878pt too wide) in paragraph at lines 111--111
+[]/cmbx12 at 17.28pt/Adding Points, Lines, and Leg-ends to Ex-ist-ing Plots 
+ []
+
+[45] [46] <figs/sample1.pdf, id=1230, 451.6875pt x 289.08pt>
+File: figs/sample1.pdf Graphic file (type pdf)
+
+<use figs/sample1.pdf> [47 <./figs/sample1.pdf>]
+<figs/sample2.pdf, id=1244, 325.215pt x 252.945pt>
+File: figs/sample2.pdf Graphic file (type pdf)
+ <use figs/sample2.pdf>
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 234--234
+[]/cmtt12/> lines(density(turnout$income))              # Adds the kernel densi
+ty line.[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 235.
+
+[48 <./figs/sample2.pdf>] <figs/ternary.pdf, id=1259, 289.08pt x 289.08pt>
+File: figs/ternary.pdf Graphic file (type pdf)
+
+<use figs/ternary.pdf> [49 <./figs/ternary.pdf>]
+<figs/roc.pdf, id=1277, 289.08pt x 289.08pt>
+File: figs/roc.pdf Graphic file (type pdf)
+ <use figs/roc.pdf> [50 <./figs/roc.pdf>] <figs/vertci.pdf, id=1294, 433.62pt x
+ 289.08pt>
+File: figs/vertci.pdf Graphic file (type pdf)
+ <use figs/vertci.pdf>)
+[51 <./figs/vertci.pdf>] [52
+
+] (./advancedR.tex
+Chapter 6.
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 27--27
+ []/cmtt12/> 2 > 3                         # Basic logical operations, includin
+g >,[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 29--29
+ []                                /cmtt12/#   <= (less than or equals), == (ex
+actly[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 31--31
+ []/cmtt12/> 3 >= 2 && 100 == 1000/10      # Advanced logical operations, inclu
+ding[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 32--32
+ []   /cmtt12/[1] TRUE                     #   & (and), && (if and only if), | 
+(or),[] 
+ []
+
+[53
+
+]
+Overfull \hbox (35.09491pt too wide) in paragraph at lines 78--78
+ []/cmtt12/> a <- 1:5               # Creates a vector of integers from 1 to 5 
+inclusive[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 84--84
+ []/cmtt12/> a[5] <- 3.14         # Inserts 3.14 as the 5th value in the vector
+ `a'[] 
+ []
+
+
+Overfull \hbox (35.09491pt too wide) in paragraph at lines 85--85
+ []/cmtt12/> a[5:7] <- c(2, 4, 7) # Replaces the 5th through 7th values with 2,
+ 4, and 7[] 
+ []
+
+[54]
+Overfull \hbox (28.9199pt too wide) in paragraph at lines 112--112
+ []/cmtt12/> x <- c("like", "dislike", "hate", "like", "don't know", "like", "d
+islike")[] 
+ []
+
+
+Overfull \hbox (11.54457pt too wide) in paragraph at lines 144--144
+ []        /cmtt12/[,1] [,2] [,3]       # Note that when assigning a vector to 
+a[] 
+ []
+
+[55]
+Overfull \hbox (17.71957pt too wide) in paragraph at lines 154--154
+ []/cmtt12/> y <- c(55, 33, 12)         # Creates another vector `y' of 3 value
+s.[] 
+ []
+
+
+Overfull \hbox (11.54457pt too wide) in paragraph at lines 155--155
+ []/cmtt12/> rbind(x, y)                # Creates a 2 x 3 matrix.  Note that ro
+w[] 
+ []
+
+
+Overfull \hbox (11.54457pt too wide) in paragraph at lines 159--159
+ []/cmtt12/> cbind(x, y)                # Creates a 3 x 2 matrix.  Note that th
+e[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 176--176
+ []/cmtt12/> loo[1,3] <- 13          # Inserts 13 as the value for row 1, colum
+n 3.[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 188--188
+ []/cmtt12/> a <- matrix(8, 2, 3)       # Creates a 2 x 3 matrix populated with
+ 8's.[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 189--189
+ []/cmtt12/> b <- matrix(9, 2, 3)       # Creates a 2 x 3 matrix populated with
+ 9's.[] 
+ []
+
+
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 191--191
+ []   /cmtt12/, , 1                     #  level [,,1] populated with matrix a 
+(8's),[] 
+ []
+
+
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 195--195
+ []   /cmtt12/[2,]    8    8    8       # Use square brackets to extract values
+.  For[] 
+ []
+
+[56]
+Overfull \hbox (22.7449pt too wide) in paragraph at lines 197--197
+ []   /cmtt12/, , 2                     #  value in the first row of the second
+ level.[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 230--230
+ []/cmtt12/> c[1, 2]     # Pull out the value in the first row, second column o
+f `c'[] 
+ []
+
+[57]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 252.
+
+
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 268--268
+[]/cmtt12/> L[[4]] <- c(1, 4, 6, 8)          # Inserts the vector into the 4th 
+position[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 272.
+
+
+Overfull \hbox (11.89522pt too wide) in paragraph at lines 291--291
+[]/cmtt12/> dat[1:10,]         # Extracts observations 1-10 and all associated 
+variables 
+ []
+
+[58] [59]
+Chapter 7.
+[60
+
+]
+LaTeX Font Info:    Try loading font information for OMS+cmtt on input line 368
+.
+LaTeX Font Info:    No file OMScmtt.fd. on input line 368.
+
+
+LaTeX Font Warning: Font shape `OMS/cmtt/m/n' undefined
+(Font)              using `OMS/cmsy/m/n' instead
+(Font)              for symbol `textbraceleft' on input line 368.
+
+
+Overfull \hbox (5.72021pt too wide) in paragraph at lines 402--402
+[]/cmtt12/> x <- array(dim = c(5,2,3))       # A 3D array of 3 stacked 5 by 2 m
+atrices.[] 
+ []
+
+[61]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 446.
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 465.
+
+[62]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 478.
+
+
+Overfull \hbox (36.98639pt too wide) in paragraph at lines 479--484
+/cmr12/The sin-gle loop pro-ce-dure eval-u-ates each el-e-ment in /cmtt12/idx /
+cmr12/against the vec-tor /cmtt12/mydata/tctt1200/$/cmtt12/state/cmr12/.
+ []
+
+
+Overfull \hbox (22.7449pt too wide) in paragraph at lines 504--504
+ []/cmtt12/z.out <- zelig(y ~ x1 + x2 + x3, by = "state", data = mydata, model 
+= "ls")[] 
+ []
+
+
+Overfull \hbox (28.9199pt too wide) in paragraph at lines 510--510
+ []/cmtt12/se <- array()                          # Initalize the empty data st
+ructure.[] 
+ []
+
+
+Overfull \hbox (22.7449pt too wide) in paragraph at lines 511--511
+ []/cmtt12/for (i in 1:50) {                      # vcov() creates the variance
+ matrix[] 
+ []
+
+
+Overfull \hbox (28.9199pt too wide) in paragraph at lines 512--512
+ []  /cmtt12/se[i] <- sqrt(vcov(z.out[[i]])[2,2]) # Since we have an intercept,
+ the 2nd[] 
+ []
+
+
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 513--513
+ []/cmtt12/}                                      # diagonal value corresponds 
+to x1.[] 
+ []
+
+[63]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 529.
+
+) (./newModels.tex [64]
+Chapter 8.
+[65
+
+]
+Overfull \hbox (11.89522pt too wide) in paragraph at lines 48--48
+[]/cmtt12/# Given the formula above, programmers can use the following standard
+ commands[] 
+ []
+
+[66]
+Overfull \hbox (1.10294pt too wide) in paragraph at lines 115--115
+[]/cmbx12 at 14.4pt/Single Re-sponse Vari-able Mod-els: Nor-mal Re-gres-sion Model
+ 
+ []
+
+
+Overfull \hbox (19.60844pt too wide) in paragraph at lines 123--127
+/cmr12/im-plies two sets of pa-ram-e-ters in your model, and the fol-low-ing /c
+mtt12/describe.normal.regression()
+ []
+
+[67] [68]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 228.
+
+[69] [70] [71] [72] [73] [74] [75]
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 588--588
+ []/cmtt12/X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "
+mu2"))[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 598--598
+ []/cmtt12/X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu
+2"))[] 
+ []
+
+) (./addingModels.tex [76]
+Chapter 9.
+
+LaTeX Warning: `!h' float specifier changed to `!ht'.
+
+[77
+
+] [78]
+Overfull \hbox (39.88014pt too wide) in paragraph at lines 143--152
+/cmtt12/zelig2normal.regression()/cmr12/, in-clud-ing the ex-tra ar-gu-ments ta
+ken by /cmtt12/normal.regression()/cmr12/,
+ []
+
+
+Overfull \hbox (6.93912pt too wide) in paragraph at lines 143--152
+/cmr12/but not by /cmtt12/zelig()/cmr12/. All wrap-pers must take the same stan
+-dard-ized ar-gu-ments (/cmtt12/formula/cmr12/,
+ []
+
+
+Overfull \hbox (59.38927pt too wide) in paragraph at lines 143--152
+/cmtt12/zelig() /cmr12/call into the /cmtt12/normal.regression() /cmr12/call. A
+d-di-tional ar-gu-ments to /cmtt12/normal.regression()/cmr12/,
+ []
+
+[79] [80]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 234.
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 240.
+
+[81] [82] [83] [84]
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 461--461
+ []/cmtt12/\item The first difference in expected values, given when x1 is spec
+ified.[] 
+ []
+
+[85]) [86] [87
+
+] (./refman.tex
+Chapter 10.
+(./commands/zelig.tex
+
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+
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+
+
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+
+
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+(hyperref)                removing `\font at name' on input line 1.
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+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
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+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
+
+[88
+
+]
+Overfull \hbox (3.76364pt too wide) in paragraph at lines 53--55
+[]/cmtt12/"irt1d"/cmr12/: Bayesian uni-di-men-sional item re-sponse the-ory mod
+el. (see Sec-tion []12.12[][]) 
+ []
+
+[89]
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 108--108
+ []/cmtt12/> z.out <- zelig(y ~ x1 + x2, data = mydata, model = "ls", by = "sta
+te")[] 
+ []
+
+
+Overfull \hbox (9.25255pt too wide) in paragraph at lines 123--129
+/cmr12/ing /cmtt12/coefficients/cmr12/, /cmtt12/residuals/cmr12/, and /cmtt12/f
+ormula /cmr12/which may be sum-ma-rized us-ing /cmtt12/summary(z.out)
+ []
+
+[90]) [91]
+\openout2 = `commands/setx.aux'.
+
+ (./commands/setx.tex
+
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+
+[92
+
+] [93]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 123.
+
+) [94]
+\openout2 = `commands/sim.aux'.
+
+ (./commands/sim.tex
+
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+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
+
+[95
+
+
+] [96]) [97]
+\openout2 = `commands/summary.aux'.
+
+ (./commands/summary.tex
+
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+
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+
+
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+
+
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+
+
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+
+
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+
+
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+
+
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+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
+
+[98
+
+
+]
+Overfull \hbox (23.3659pt too wide) in paragraph at lines 100--107
+/cmr12/statis-tics for each of the quan-ti-ties of in-ter-est. For ex-am-ple, /
+cmtt12/summary(s.out)/tctt1200/$/cmtt12/qi.stats/tctt1200/$/cmtt12/ev/cmr12/,
+ []
+
+) [99] [100]
+\openout2 = `commands/plot.zelig.aux'.
+
+ (./commands/plot.zelig.tex
+
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+(hyperref)                removing `\ignorespaces' on input line 1.
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+[101
+
+
+]) [102]
+\openout2 = `commands/print.aux'.
+
+ (./commands/print.tex
+
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+
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+(hyperref)                removing `\ignorespaces' on input line 1.
+
+) [103
+
+
+]
+\openout2 = `commands/replicate.aux'.
+
+ (./commands/replicate.tex
+
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+(hyperref)                removing `\ignorespaces' on input line 1.
+
+[104
+
+
+]
+Overfull \hbox (30.42023pt too wide) in paragraph at lines 60--60
+[]/cmtt12/> z.out <- zelig(vote ~ race + educate, model = "logit", data = turno
+ut[1:1000,])[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 68.
+
+) [105]
+Chapter 11.
+(./commands/matchit.tex
+
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+]) [157]
+\openout2 = `models/exp.aux'.
+
+ (./models/exp.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
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+(hyperref)                removing `\<def>-command' on input line 2.
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+(hyperref)                removing `\<def>-command' on input line 2.
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+(hyperref)                removing `\font at name' on input line 2.
+
+
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+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[158
+
+
+] [159]
+! pdfTeX warning (ext4): destination with the same identifier (name{equation.12
+.1}) has been already used, duplicate ignored
+<to be read again> 
+                   \penalty 
+l.146 \begin{equation}
+                       [160]
+Overfull \hbox (8.84404pt too wide) in paragraph at lines 185--193
+/cmr12/may ex-am-ine the avail-able in-for-ma-tion in /cmtt12/z.out /cmr12/by u
+s-ing /cmtt12/names(z.out)/cmr12/, see the /cmtt12/coefficients
+ []
+
+
+Overfull \hbox (17.41278pt too wide) in paragraph at lines 185--193
+/cmr12/by us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-
+fault sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+
+Overfull \hbox (34.08284pt too wide) in paragraph at lines 215--218
+[]/cmr12/Most of this may be con-ve-niently sum-ma-rized us-ing /cmtt12/summary
+(z.out)/cmr12/. From /cmtt12/summary(z.out)/cmr12/,
+ []
+
+[161]) [162]
+\openout2 = `models/factor.bayes.aux'.
+
+ (./models/factor.bayes.tex
+Overfull \hbox (7.78902pt too wide) in paragraph at lines 41--43
+[]/cmtt12/varname = list(d, c)/cmr12/: con-strains the $/cmmi12/d$/cmr12/th loa
+d-ing for the vari-able named /cmtt12/varname
+ []
+
+[163
+
+
+] (./models/coda_diag.tex [164])
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 153.
+
+[165]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 173.
+
+[166] (./models/contributors.tex)) [167]
+\openout2 = `models/factor.mix.aux'.
+
+ (./models/factor.mix.tex
+Overfull \hbox (7.78902pt too wide) in paragraph at lines 43--45
+[]/cmtt12/varname = list(d, c)/cmr12/: con-strains the $/cmmi12/d$/cmr12/th loa
+d-ing for the vari-able named /cmtt12/varname
+ []
+
+[168
+
+
+] [169] (./models/coda_diag.tex) [170] [171] (./models/contributors.tex
+[172])) [173]
+\openout2 = `models/factor.ord.aux'.
+
+ (./models/factor.ord.tex
+Overfull \hbox (7.78902pt too wide) in paragraph at lines 38--40
+[]/cmtt12/varname = list(d, c)/cmr12/: con-strains the $/cmmi12/d$/cmr12/th loa
+d-ing for the vari-able named /cmtt12/varname
+ []
+
+[174
+
+
+] (./models/coda_diag.tex [175])
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 138.
+
+[176] [177] (./models/contributors.tex)) [178]
+\openout2 = `models/gamma.aux'.
+
+ (./models/gamma.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at family' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at series' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at shape' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
+
+
+Overfull \hbox (11.89522pt too wide) in paragraph at lines 59--59
+[]/cmtt12/> z.out <- zelig(duration ~ fract + numst2, model = "gamma", data = c
+oalition)[] 
+ []
+
+[179
+
+
+]
+Underfull \hbox (badness 10000) in paragraph at lines 88--90
+
+ []
+
+[180] [181] [182]) [183]
+\openout2 = `models/irt1d.aux'.
+
+ (./models/irt1d.tex [184
+
+
+] (./models/coda_diag.tex
+[185])
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 138--138
+ []                         /cmtt12/"Kennedy","Souter","Thomas","Ginsburg","Bre
+yer")[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 145--145
+ []               /cmtt12/B0.alpha = 0.2, B0.beta = 0.2, burnin = 500, mcmc = 1
+0000,[] 
+ []
+
+[186] [187] (./models/contributors.tex)) [188]
+\openout2 = `models/irtkd.aux'.
+
+ (./models/irtkd.tex
+Overfull \hbox (23.31868pt too wide) in paragraph at lines 1--1
+[]/cmtt12 at 17.28pt/irtkd/cmbx12 at 17.28pt/: $/cmmi12 at 17.28pt/k$/cmbx12 at 17.28pt/-Di
+mensional Item Re-sponse The-ory Model 
+ []
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `math shift' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `math shift' on input line 1.
+
+[189
+
+
+] (./models/coda_diag.tex [190])
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 137--137
+ []                         /cmtt12/"Kennedy","Souter","Thomas","Ginsburg","Bre
+yer")[] 
+ []
+
+
+Overfull \hbox (28.9199pt too wide) in paragraph at lines 141--141
+ []/cmtt12/> z.out <- zelig(cbind(Rehnquist, Stevens, OConnor, Scalia, Kennedy,
+ Souter,[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 142--142
+ []                       /cmtt12/Thomas, Ginsburg, Breyer) ~ NULL, dimensions 
+= 1,[] 
+ []
+
+[191]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 154.
+
+[192] (./models/contributors.tex)) [193]
+\openout2 = `models/logit.aux'.
+
+ (./models/logit.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at family' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at series' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at shape' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 2.
+
+
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+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 60--60
+ []/cmtt12/> z.out1 <- zelig(vote ~ race + educate,  model = "logit", data = tu
+rnout)[] 
+ []
+
+[194
+
+
+]
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 80--80
+ []/cmtt12/> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0
+.75))[] 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 81--81
+ []/cmtt12/> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.
+25))[] 
+ []
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 98--98
+ []/cmtt12/> z.out2 <- zelig(vote ~ race + educate, model = "logit", data = tur
+nout)[] 
+ []
+
+[195] [196] [197]) [198]
+\openout2 = `models/logit.bayes.aux'.
+
+ (./models/logit.bayes.tex (./models/coda_diag.tex
+[199
+
+
+])
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 88.
+
+[200]
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 105--105
+ []/cmtt12/> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0
+.75)) 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 106--106
+ []/cmtt12/> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.
+25))[] 
+ []
+
+[201] [202] (./models/contributors2.tex)) [203]
+\openout2 = `models/lognormal.aux'.
+
+ (./models/lognormal.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+(hyperref)                removing `\f at family' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+(hyperref)                removing `\f at series' on input line 2.
+
+
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+(hyperref)                removing `\<def>-command' on input line 2.
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+(hyperref)                removing `\<def>-command' on input line 2.
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+(hyperref)                removing `\font at name' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[204
+
+
+] [205] [206]
+Overfull \hbox (29.73271pt too wide) in paragraph at lines 183--191
+/cmr12/you may ex-am-ine the avail-able in-for-ma-tion in /cmtt12/z.out /cmr12/
+by us-ing /cmtt12/names(z.out)/cmr12/, see the /cmtt12/coefficients
+ []
+
+
+Overfull \hbox (17.41278pt too wide) in paragraph at lines 183--191
+/cmr12/by us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-
+fault sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+
+Overfull \hbox (34.08284pt too wide) in paragraph at lines 209--212
+[]/cmr12/Most of this may be con-ve-niently sum-ma-rized us-ing /cmtt12/summary
+(z.out)/cmr12/. From /cmtt12/summary(z.out)/cmr12/,
+ []
+
+[207]) [208]
+\openout2 = `models/ls.aux'.
+
+ (./models/ls.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at family' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at series' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at shape' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[209
+
+
+]
+Overfull \hbox (16.5699pt too wide) in paragraph at lines 61--61
+ []/cmtt12/> z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = 
+macro)[] 
+ []
+
+[210] [211] [212]) [213]
+\openout2 = `models/mlogit.aux'.
+
+ (./models/mlogit.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at family' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at series' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at shape' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+
+Overfull \hbox (50.7404pt too wide) in paragraph at lines 19--23
+/cmr12/If the user wishes to use the same for-mula across all lev-els, then []/
+cmtt12/formula <- as.factor(Y) ~ X1 + X2
+ []
+
+[214
+
+
+] [215] [216]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 170--178
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[217]) [218]
+\openout2 = `models/mlogit.bayes.aux'.
+
+ (./models/mlogit.bayes.tex
+Overfull \hbox (1.43478pt too wide) in paragraph at lines 2--2
+[]/cmtt12 at 17.28pt/mlogit.bayes/cmbx12 at 17.28pt/: Bayesian Multi-no-mial Lo-gis-t
+ic Re-
+ []
+
+[219
+
+
+] (./models/coda_diag.tex) [220]
+Overfull \hbox (35.09491pt too wide) in paragraph at lines 94--94
+ []/cmtt12/> z.out <- zelig(vote88 ~ pristr + othcok + othsocok, model = "mlogi
+t.bayes",[] 
+ []
+
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 102.
+
+[221] [222] [223] (./models/contributors2.tex)) [224]
+\openout2 = `models/negbin.aux'.
+
+ (./models/negbin.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at family' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at series' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at shape' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[225
+
+
+] [226]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 144--152
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[227] [228]) [229]
+\openout2 = `models/normal.aux'.
+
+ (./models/normal.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at encoding' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<let>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
+
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+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
+
+[230
+
+
+] [231] [232]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 176--184
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[233]) [234]
+\openout2 = `models/normal.bayes.aux'.
+
+ (./models/normal.bayes.tex [235
+
+
+] (./models/coda_diag.tex) [236]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 89.
+
+[237] [238] (./models/contributors2.tex)) [239]
+\openout2 = `models/ologit.aux'.
+
+ (./models/ologit.tex
+
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+(hyperref)                removing `\<def>-command' on input line 2.
+
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+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[240
+
+
+] [241]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 162--170
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[242]) [243] [244]
+\openout2 = `models/oprobit.aux'.
+
+ (./models/oprobit.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
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+
+
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+(hyperref)                removing `\f at family' on input line 2.
+
+
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+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+(hyperref)                removing `\f at series' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\f at shape' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
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+
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+
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+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 41--41
+ []/cmtt12/> z.out <- zelig(ncost ~ mil + coop, model = "oprobit", data = sanct
+ion)[] 
+ []
+
+[245
+
+
+] [246]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 163--171
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[247] [248]) [249]
+\openout2 = `models/oprobit.bayes.aux'.
+
+ (./models/oprobit.bayes.tex (./models/coda_diag.tex [250
+
+
+])
+[251]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 94.
+
+[252] [253] [254] (./models/contributors2.tex)) [255]
+\openout2 = `models/poisson.aux'.
+
+ (./models/poisson.tex
+
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+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[256
+
+
+] [257]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 136--144
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[258]) [259]
+\openout2 = `models/poisson.bayes.aux'.
+
+ (./models/poisson.bayes.tex (./models/coda_diag.tex [260
+
+
+])
+[261]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 90.
+
+[262] [263] (./models/contributors2.tex)) [264]
+\openout2 = `models/probit.aux'.
+
+ (./models/probit.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 1.
+
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+
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+
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+(hyperref)                removing `\<def>-command' on input line 1.
+
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+
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+(hyperref)                removing `\<def>-command' on input line 1.
+
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+(hyperref)                removing `\f at series' on input line 1.
+
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+(hyperref)                removing `\<def>-command' on input line 1.
+
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+(hyperref)                removing `\f at shape' on input line 1.
+
+
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+(hyperref)                removing `\<def>-command' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\font at name' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 1.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
+
+[265
+
+
+] [266]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 136--144
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[267] [268]) [269]
+\openout2 = `models/probit.bayes.aux'.
+
+ (./models/probit.bayes.tex [270
+
+
+] (./models/coda_diag.tex)
+[271]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 93.
+
+
+Overfull \hbox (10.3949pt too wide) in paragraph at lines 110--110
+ []/cmtt12/> x.high <- setx(z.out, educate = quantile(turnout$educate, prob = 0
+.75)) 
+ []
+
+
+Overfull \hbox (4.2199pt too wide) in paragraph at lines 111--111
+ []/cmtt12/> x.low <- setx(z.out, educate = quantile(turnout$educate, prob = 0.
+25))[] 
+ []
+
+[272] [273] (./models/contributors2.tex)) [274]
+\openout2 = `models/relogit.aux'.
+
+ (./models/relogit.tex
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+
+
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+
+
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+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+(hyperref)                removing `\f at family' on input line 2.
+
+
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+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+(hyperref)                removing `\f at series' on input line 2.
+
+
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+(hyperref)                removing `\<def>-command' on input line 2.
+
+
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+(hyperref)                removing `\f at shape' on input line 2.
+
+
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+(hyperref)                removing `\<def>-command' on input line 2.
+
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+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[275
+
+
+]
+Overfull \hbox (11.89522pt too wide) in paragraph at lines 85--85
+[]/cmtt12/> z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem
+ + years,[] 
+ []
+
+
+Overfull \hbox (11.89522pt too wide) in paragraph at lines 111--111
+[]/cmtt12/> z.out2 <- zelig(conflict ~ major + contig + power + maxdem + mindem
+ + years,[] 
+ []
+
+[276] [277] [278] [279]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 295--303
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+[280] [281]
+Overfull \hbox (72.13203pt too wide) in paragraph at lines 379--380
+/cmr12/tions,'' /cmti12/In-ter-na-tional Or-ga-ni-za-tion/cmr12/, 55, 693--715,
+ http://gking.harvard.edu/files/abs/baby0s-
+ []
+
+
+Overfull \hbox (85.63pt too wide) in paragraph at lines 385--386
+/cmr12/Control Stud-ies,'' /cmti12/Statis-tics in Medicine/cmr12/, 21, 1409--14
+27, http://gking.harvard.edu/files/abs/1s-
+ []
+
+
+Overfull \hbox (72.13203pt too wide) in paragraph at lines 390--391
+/cmr12/tions,'' /cmti12/In-ter-na-tional Or-ga-ni-za-tion/cmr12/, 55, 693--715,
+ http://gking.harvard.edu/files/abs/baby0s-
+ []
+
+) [282]
+\openout2 = `models/tobit.aux'.
+
+ (./models/tobit.tex [283
+
+
+] [284] [285]) [286]
+\openout2 = `models/tobit.bayes.aux'.
+
+ (./models/tobit.bayes.tex
+[287
+
+
+] (./models/coda_diag.tex) [288]
+
+LaTeX Warning: Characters dropped after `\end{verbatim}' on input line 103.
+
+[289]
+Overfull \hbox (15.78479pt too wide) in alignment at lines 186--189
+ [][][] []
+ []
+
+[290] (./models/contributors.tex [291])) [292]
+\openout2 = `models/weibull.aux'.
+
+ (./models/weibull.tex
+
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+(hyperref)                removing `\font at name' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\OT1/cmtt/m/n/17.28' on input line 2.
+
+
+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[293
+
+
+] [294] [295]
+Overfull \hbox (2.39905pt too wide) in paragraph at lines 184--192
+/cmr12/us-ing /cmtt12/z.out/tctt1200/$/cmtt12/coefficients/cmr12/, and a de-fau
+lt sum-mary of in-for-ma-tion through /cmtt12/summary(z.out)/cmr12/.
+ []
+
+
+Overfull \hbox (34.08284pt too wide) in paragraph at lines 210--213
+[]/cmr12/Most of this may be con-ve-niently sum-ma-rized us-ing /cmtt12/summary
+(z.out)/cmr12/. From /cmtt12/summary(z.out)/cmr12/,
+ []
+
+[296]) [297]
+Chapter 13.
+(./commands/describe.mymodel.tex
+Overfull \hbox (25.92883pt too wide) in paragraph at lines 2--2
+[]/cmtt12 at 17.28pt/describe/cmbx12 at 17.28pt/: De-scribe a model's sys-tem-atic an
+d stochas-
+ []
+
+
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+(hyperref)                removing `\ignorespaces' on input line 2.
+
+[298
+
+
+] [299] [300]
+Overfull \hbox (15.58759pt too wide) in paragraph at lines 194--196
+[]/cmr12/Section []8[][] for an overview of how the /cmtt12/describe.*() /cmr12
+/func-tion works with /cmtt12/parse.formula()/cmr12/. 
+ []
+
+[301]) [302]
+\openout2 = `commands/model.end.aux'.
+
+ (./commands/model.end.tex
+
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+Package hyperref Warning: Token not allowed in a PDFDocEncoded string,
+(hyperref)                removing `\ignorespaces' on input line 1.
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+) [303
+
+]
+\openout2 = `commands/model.frame.multiple.aux'.
+
+ (./commands/model.frame.multiple.tex
+
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@@ -0,0 +1,130 @@
+\BOOKMARK [0][-]{chapter.1}{Introduction}{}
+\BOOKMARK [1][-]{section.1.1}{What Zelig and R Do}{chapter.1}
+\BOOKMARK [1][-]{section.1.2}{Getting Help}{chapter.1}
+\BOOKMARK [-1][-]{part.1}{I User's Guide}{}
+\BOOKMARK [0][-]{chapter.2}{Installation}{part.1}
+\BOOKMARK [1][-]{section.2.1}{Windows}{chapter.2}
+\BOOKMARK [1][-]{section.2.2}{MacOS X}{chapter.2}
+\BOOKMARK [1][-]{section.2.3}{UNIX and Linux}{chapter.2}
+\BOOKMARK [1][-]{section.2.4}{Version Compatability}{chapter.2}
+\BOOKMARK [0][-]{chapter.3}{Data Analysis Commands}{part.1}
+\BOOKMARK [1][-]{section.3.1}{Command Syntax}{chapter.3}
+\BOOKMARK [2][-]{subsection.3.1.1}{Getting Started}{section.3.1}
+\BOOKMARK [2][-]{subsection.3.1.2}{Details}{section.3.1}
+\BOOKMARK [1][-]{section.3.2}{Data Sets}{chapter.3}
+\BOOKMARK [2][-]{subsection.3.2.1}{Data Structures}{section.3.2}
+\BOOKMARK [2][-]{subsection.3.2.2}{Loading Data}{section.3.2}
+\BOOKMARK [2][-]{subsection.3.2.3}{Saving Data}{section.3.2}
+\BOOKMARK [1][-]{section.3.3}{Variables}{chapter.3}
+\BOOKMARK [2][-]{subsection.3.3.1}{Classes of Variables}{section.3.3}
+\BOOKMARK [2][-]{subsection.3.3.2}{Recoding Variables}{section.3.3}
+\BOOKMARK [0][-]{chapter.4}{Statistical Commands}{part.1}
+\BOOKMARK [1][-]{section.4.1}{Zelig Commands}{chapter.4}
+\BOOKMARK [2][-]{subsection.4.1.1}{Quick Overview}{section.4.1}
+\BOOKMARK [2][-]{subsection.4.1.2}{Examples}{section.4.1}
+\BOOKMARK [2][-]{subsection.4.1.3}{Details}{section.4.1}
+\BOOKMARK [1][-]{section.4.2}{Supported Models}{chapter.4}
+\BOOKMARK [1][-]{section.4.3}{Replication Procedures}{chapter.4}
+\BOOKMARK [2][-]{subsection.4.3.1}{Saving Replication Materials}{section.4.3}
+\BOOKMARK [2][-]{subsection.4.3.2}{Replicating Analyses}{section.4.3}
+\BOOKMARK [0][-]{chapter.5}{Graphing Commands}{part.1}
+\BOOKMARK [1][-]{section.5.1}{Drawing Plots}{chapter.5}
+\BOOKMARK [1][-]{section.5.2}{Adding Points, Lines, and Legends to Existing Plots}{chapter.5}
+\BOOKMARK [1][-]{section.5.3}{Saving Graphs to Files}{chapter.5}
+\BOOKMARK [1][-]{section.5.4}{Examples}{chapter.5}
+\BOOKMARK [2][-]{subsection.5.4.1}{Descriptive Plots: Box-plots}{section.5.4}
+\BOOKMARK [2][-]{subsection.5.4.2}{Density Plots: A Histogram}{section.5.4}
+\BOOKMARK [2][-]{subsection.5.4.3}{Advanced Examples}{section.5.4}
+\BOOKMARK [-1][-]{part.2}{II Advanced Zelig Uses}{}
+\BOOKMARK [0][-]{chapter.6}{R Objects}{part.2}
+\BOOKMARK [1][-]{section.6.1}{Scalar Values}{chapter.6}
+\BOOKMARK [1][-]{section.6.2}{Data Structures}{chapter.6}
+\BOOKMARK [2][-]{subsection.6.2.1}{Arrays}{section.6.2}
+\BOOKMARK [2][-]{subsection.6.2.2}{Lists}{section.6.2}
+\BOOKMARK [2][-]{subsection.6.2.3}{Data Frames}{section.6.2}
+\BOOKMARK [2][-]{subsection.6.2.4}{Identifying Objects and Data Structures}{section.6.2}
+\BOOKMARK [0][-]{chapter.7}{Programming Statements}{part.2}
+\BOOKMARK [1][-]{section.7.1}{Functions}{chapter.7}
+\BOOKMARK [1][-]{section.7.2}{If-Statements}{chapter.7}
+\BOOKMARK [1][-]{section.7.3}{For-Loops}{chapter.7}
+\BOOKMARK [0][-]{chapter.8}{Writing New Models}{part.2}
+\BOOKMARK [1][-]{section.8.1}{Managing Statistical Model Inputs}{chapter.8}
+\BOOKMARK [2][-]{subsection.8.1.1}{Describe the Statistical Model}{section.8.1}
+\BOOKMARK [2][-]{subsection.8.1.2}{Single Response Variable Models: Normal Regression Model}{section.8.1}
+\BOOKMARK [2][-]{subsection.8.1.3}{Multivariate models: Bivariate Normal example}{section.8.1}
+\BOOKMARK [1][-]{section.8.2}{Easy Ways to Manage Matrices}{chapter.8}
+\BOOKMARK [2][-]{subsection.8.2.1}{The Intuitive Layout}{section.8.2}
+\BOOKMARK [2][-]{subsection.8.2.2}{The Computationally-Efficient Layout}{section.8.2}
+\BOOKMARK [2][-]{subsection.8.2.3}{The Memory-Efficient Layout}{section.8.2}
+\BOOKMARK [2][-]{subsection.8.2.4}{Interchanging the Three Methods}{section.8.2}
+\BOOKMARK [0][-]{chapter.9}{Adding Models and Methods to Zelig}{part.2}
+\BOOKMARK [1][-]{section.9.1}{Making the Model Compatible with Zelig}{chapter.9}
+\BOOKMARK [1][-]{section.9.2}{Getting Ready for the GUI}{chapter.9}
+\BOOKMARK [1][-]{section.9.3}{Formatting Reference Manual Pages}{chapter.9}
+\BOOKMARK [-1][-]{part.3}{III Reference Manual}{}
+\BOOKMARK [0][-]{chapter.10}{Main Commands}{part.3}
+\BOOKMARK [1][-]{section.10.1}{zelig: Estimating a Statistical Model}{chapter.10}
+\BOOKMARK [1][-]{section.10.2}{setx: Setting Explanatory Variable Values}{chapter.10}
+\BOOKMARK [1][-]{section.10.3}{sim: Simulating Quantities of Interest}{chapter.10}
+\BOOKMARK [1][-]{section.10.4}{summary: Summarizing Zelig Output}{chapter.10}
+\BOOKMARK [1][-]{section.10.5}{plot: Graphing Quantities of Interest}{chapter.10}
+\BOOKMARK [1][-]{section.10.6}{print: Printing Quantities of Interest}{chapter.10}
+\BOOKMARK [1][-]{section.10.7}{repl: Replicating Analyses}{chapter.10}
+\BOOKMARK [0][-]{chapter.11}{Supplementary Commands}{part.3}
+\BOOKMARK [1][-]{section.11.1}{matchit: Create matched data}{chapter.11}
+\BOOKMARK [1][-]{section.11.2}{mi: Create a list of multiply imputed data frames}{chapter.11}
+\BOOKMARK [1][-]{section.11.3}{plot.ci: Plotting Vertical confidence Intervals}{chapter.11}
+\BOOKMARK [1][-]{section.11.4}{rocplot: Receiver Operator Characteristic Plots}{chapter.11}
+\BOOKMARK [1][-]{section.11.5}{ternaryplot: Ternary Diagram for 3D Data}{chapter.11}
+\BOOKMARK [1][-]{section.11.6}{ternarypoints: Adding Points to Ternary Diagrams}{chapter.11}
+\BOOKMARK [0][-]{chapter.12}{Models Zelig Can Run}{part.3}
+\BOOKMARK [1][-]{section.12.1}{arima: arima Models for Time Series Data}{chapter.12}
+\BOOKMARK [1][-]{section.12.2}{blogit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.3}{bprobit: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.4}{ei.dynamic: Quinn's Dynamic Ecological Inference Model}{chapter.12}
+\BOOKMARK [1][-]{section.12.5}{ei.hier: Hierarchical Ecological Inference Model for 2 2 Tables}{chapter.12}
+\BOOKMARK [1][-]{section.12.6}{ei.RxC: Hierarchical Multinomial-Dirichlet Ecological Inference Model for R C Tables}{chapter.12}
+\BOOKMARK [1][-]{section.12.7}{exp: Exponential Regression for Duration Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.8}{factor.bayes: Bayesian Factor Analysis}{chapter.12}
+\BOOKMARK [1][-]{section.12.9}{factor.mix: Mixed Data Factor Analysis}{chapter.12}
+\BOOKMARK [1][-]{section.12.10}{factor.ord: Ordinal Data Factor Analysis}{chapter.12}
+\BOOKMARK [1][-]{section.12.11}{gamma: Gamma Regression for Continuous, Positive Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.12}{irt1d: One Dimensional Item Response Model}{chapter.12}
+\BOOKMARK [1][-]{section.12.13}{irtkd: k-Dimensional Item Response Theory Model}{chapter.12}
+\BOOKMARK [1][-]{section.12.14}{logit: Logistic Regression for Dichotomous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.15}{logit.bayes: Bayesian Logistic Regression}{chapter.12}
+\BOOKMARK [1][-]{section.12.16}{lognorm: Log-Normal Regression for Duration Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.17}{ls: Least Squares Regression for Continuous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.18}{mlogit: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values}{chapter.12}
+\BOOKMARK [1][-]{section.12.19}{mlogit.bayes: Bayesian Multinomial Logistic Regression}{chapter.12}
+\BOOKMARK [1][-]{section.12.20}{negbin: Negative Binomial Regression for Event Count Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.21}{normal: Normal Regression for Continuous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.22}{normal.bayes: Bayesian Normal Linear Regression}{chapter.12}
+\BOOKMARK [1][-]{section.12.23}{ologit: Ordinal Logistic Regression for Ordered Categorical Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.24}{oprobit: Ordinal Probit Regression for Ordered Categorical Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.25}{oprobit.bayes: Bayesian Ordered Probit Regression}{chapter.12}
+\BOOKMARK [1][-]{section.12.26}{poisson: Poisson Regression for Event Count Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.27}{poisson.bayes: Bayesian Poisson Regression}{chapter.12}
+\BOOKMARK [1][-]{section.12.28}{probit: Probit Regression for Dichotomous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.29}{probit.bayes: Bayesian Probit Regression}{chapter.12}
+\BOOKMARK [1][-]{section.12.30}{relogit: Rare Events Logistic Regression for Dichotomous Dependent Variables}{chapter.12}
+\BOOKMARK [1][-]{section.12.31}{tobit: Linear Regression for a Left-Censored Dependent Variable}{chapter.12}
+\BOOKMARK [1][-]{section.12.32}{tobit.bayes: Bayesian Linear Regression for a Censored Dependent Variable}{chapter.12}
+\BOOKMARK [1][-]{section.12.33}{weibull: Weibull Regression for Duration Dependent Variables}{chapter.12}
+\BOOKMARK [0][-]{chapter.13}{Commands for Programmers and Contributors}{part.3}
+\BOOKMARK [1][-]{section.13.1}{describe: Describe a model's systematic and stochastic parameters}{chapter.13}
+\BOOKMARK [1][-]{section.13.2}{model.end: Cleaning up after optimization}{chapter.13}
+\BOOKMARK [1][-]{section.13.3}{model.frame.multiple: Extracting the ``environment'' of a model formula}{chapter.13}
+\BOOKMARK [1][-]{section.13.4}{model.matrix.multiple: Design matrix for multivariate models}{chapter.13}
+\BOOKMARK [1][-]{section.13.5}{parse.formula: Parsing the inputs}{chapter.13}
+\BOOKMARK [1][-]{section.13.6}{parse.par: Select and reshape parameter vectors}{chapter.13}
+\BOOKMARK [1][-]{section.13.7}{put.start: Set specific starting values for certain parameters}{chapter.13}
+\BOOKMARK [1][-]{section.13.8}{set.start: Set starting values for all parameters}{chapter.13}
+\BOOKMARK [1][-]{section.13.9}{tag: Constrain parameter effects across equations}{chapter.13}
+\BOOKMARK [-1][-]{part.4}{IV Appendices}{}
+\BOOKMARK [0][-]{appendix.A}{Frequently Asked Questions}{part.4}
+\BOOKMARK [1][-]{section.A.1}{For All Zelig Users}{appendix.A}
+\BOOKMARK [1][-]{section.A.2}{For Zelig Contributors}{appendix.A}
+\BOOKMARK [0][-]{appendix.B}{What's New? What's Next?}{part.4}
+\BOOKMARK [1][-]{section.B.1}{What's New: Zelig Release Notes}{appendix.B}
+\BOOKMARK [1][-]{section.B.2}{What's Next?}{appendix.B}
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+trailer
+<<
+/Size 3443
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+/Info 3442 0 R
+>>
+startxref
+1328735
+%%EOF
diff --git a/inst/doc/zelig.tex b/inst/doc/zelig.tex
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+\documentclass[oneside,letterpaper,12pt]{book}
+%\usepackage[ae,hyper]{Rd}
+\usepackage{bibentry}
+\usepackage{upquote}
+\usepackage{graphicx}
+\usepackage{natbib}
+\usepackage[reqno]{amsmath}
+\usepackage{amssymb}
+\usepackage{amsfonts}
+\usepackage{amsmath}
+\usepackage{verbatim}
+\usepackage{epsf}
+\usepackage{url}
+\usepackage{html}
+\usepackage{dcolumn}
+\usepackage{multirow}
+\usepackage{fullpage}
+\usepackage{lscape}
+\usepackage[all]{xy}
+% \usepackage[pdftex, bookmarksopen=true,bookmarksnumbered=true,
+%   linkcolor=webred]{hyperref}
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+\newcolumntype{d}[1]{D{.}{.}{#1}}
+\htmladdtonavigation{
+  \htmladdnormallink{%
+    \htmladdimg{http://gking.harvard.edu/pics/home.gif}}
+  {http://gking.harvard.edu/}}
+\newcommand{\MatchIt}{{\sc MatchIt}}
+\newcommand{\hlink}{\htmladdnormallink}
+\newcommand{\Sref}[1]{Section~\ref{#1}}
+\newcommand{\fullrvers}{2.3.1}
+\newcommand{\rvers}{2.3}
+\newcommand{\rwvers}{R-2.3.1}
+%\renewcommand{\bibentry}{\citealt}
+
+\bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.jpg"}
+\setcounter{tocdepth}{2}
+
+\title{Zelig: Everyone's Statistical Software\thanks{The current
+    version of this software is available at
+    \texttt{http://gking.harvard.edu/zelig/}, free of charge and
+    open-source (under the terms of the GNU GPL, v. 2).}}
+\author{Kosuke
+  Imai\thanks{Assistant Professor, Department of Politics, Princeton
+    University (Corwin Hall, Department of Politics, Princeton
+    University, Princeton NJ 08544; \texttt{http://Imai.Princeton.Edu},
+    \texttt{KImai at Princeton.Edu}).}
+\and %
+Gary King\thanks{David Florence Professor of Government, Harvard
+  University (Institute for Quantitative Social Sciences, 1737 Cambridge 
+Street, Harvard University, Cambridge MA 02138;
+  \texttt{http://GKing.Harvard.Edu}, \texttt{King at Harvard.Edu}, (617)
+  495-2027).}
+\and %
+Olivia Lau\thanks{Ph.D.\ Candidate, Department of Government, Harvard
+  University (1737 Cambridge Street, Cambridge MA 02138;
+  \texttt{http://www.people.fas.harvard.edu/\~\,olau},
+  \texttt{OLau at Fas.Harvard.Edu}).}}
+
+% rbuild: replace 'Version ' '\\' Version
+\date{Version 2.7-1\\ \today}
+
+\begin{document}
+\maketitle
+\begin{rawhtml}
+  <p>
+  [Also available is a downloadable <a href="/zelig/docs/zelig.pdf">PDF</a>
+  version of this entire document]
+\end{rawhtml}
+
+\tableofcontents
+
+\nobibliography*
+
+\include{acknowledgments}
+
+\input{intro}
+
+\part[User's Guide]{User's Guide}
+
+\input{install}
+\input{syntax}
+\input{Zcommands}
+\input{graphs}
+
+\part{Advanced Zelig Uses}
+
+\input{advancedR}
+\input{newModels}
+\input{addingModels}
+
+\part{Reference Manual}
+
+\input{refman}
+
+\appendix
+
+\part[Appendices]{Appendices}
+
+%\input{distributions}
+
+\input{faq}
+
+\chapter{What's New?  What's Next?}
+
+\section{What's New:  Zelig Release Notes}\label{release.notes}
+
+Releases listed as ``stable releases'' have been tested against prior
+versions of Zelig for consistency and accuracy.  Testing distributions
+may contain bugs, but are usually replaced by stable releases within a
+few days. 
+
+\begin{itemize} 
+  
+\item {\bf 2.7-1} (November 3, 2006): Stable release for R 2.4.0. Made
+  changes regarding the S4 classes in VGAM. The ARIMA ({\tt arima})
+  model for time series data added by Justin Grimmer.  First level
+  dependencies are as follows:\newline
+\begin{tabular}{ll}\label{table.compat}
+     MASS      &     7.2-29 \\
+     boot      &    1.2-26 \\
+     VGAM      &    0.7-1 \\
+     MCMCpack  &    0.7-4 \\
+     mvtnorm   &    0.7-5 \\
+     survival  &    2.29 \\
+     sandwich  &    2.0-0 \\
+     zoo       &    1.2-1 \\
+     coda      &    0.10-7 \\
+\end{tabular} 
+
+
+\item {\bf 2.6-5} (September 14, 2006): Stable release for R 2.3.0-2.3.1.  Fixed bugs
+  in bivariate logit, bivariate probit, multinomial logit, and
+  model.matrix.multiple (related to changes in version 2.6-4, but not
+  previous versions, thanks to Chris Lawrence).  First level 
+  dependencies are as follows:\newline
+\begin{tabular}{ll}\label{table.compat}
+     MASS      &     7.2-27.1 \\
+     boot      &    1.2-26 \\
+     VGAM      &    0.6-9 \\
+     MCMCpack  &    0.7-1 \\
+     mvtnorm   &    0.7-2 \\
+     survival  &    2.28 \\
+     sandwich  &    1.1-1 \\
+     zoo       &    1.0-6 \\
+     coda      &    0.10-5 \\
+\end{tabular} 
+
+\item {\bf 2.6-4} (September 8, 2006): Stable release for R
+2.3.0-2.3.1.  Fixed bugs in {\tt setx()}, and bugs related to {\tt
+multiple} and the multinomial logit model.  Added instructions for
+installing Fortran tools for Intel macs.  Added the R$\times$C
+ecological inference model.  (thanks to Kurt Hornik, Luke Keele, Joerg
+Mueller-Scheessel, and B. Dan Wood)
+
+\item {\bf 2.6-3} (June 19, 2006): Stable release for R 2.0.0-2.3.1.  
+Fixed bug in {\sc vdc} interface functions, and {\tt parse.formula()}.  
+(thanks to Micah Altman, Christopher N. Lawrence, and Eric Kostello)
+
+\item {\bf 2.6-2} (June 7, 2006): Stable release for R 2.0.0-2.3.1.
+Removed R $\times$ C {\sc ei}.  Changed {\tt data = list()} to {\tt
+data = mi()} for multiply-imputed data frames.  First level version
+compatibilities are as for version 2.6-1.  
+
+\item {\bf 2.6-1} (April 29, 2006): Stable release for R 2.0.0-2.2.1.
+Fixed major bug in ordinal logit and ordinal probit expected value
+simulation procedure (does not affect Bayesian ordinal probit).
+(reported by Ian Yohai) Added the following ecological inference {\sc
+ei} models: Bayesian hierarchical {\sc ei}, Bayesian dynamic {\sc ei},
+and R $\times$ C {\sc ei}.  First level version compatibilities (at
+time of release) are as follows:\newline
+\begin{tabular}{ll}
+MASS      &    7.2-24 \\
+boot      &    1.2-24  \\        
+VGAM      &    0.6-8   \\       
+MCMCpack  &    0.7-1   \\       
+mvtnorm   &    0.7-2   \\       
+survival  &    2.24    \\       
+sandwich  &    1.1-1   \\       
+zoo       &    1.0-6   \\       
+coda      &    0.10-5  \\
+\end{tabular}
+
+\item {\bf 2.5-4} (March 16, 2006): Stable release for R 2.0.0-2.2.1.  Fixed bug 
+related to windows build.  First-level dependencies are the same as in version 
+2.5-1.
+
+\item {\bf 2.5-3} (March 9, 2006): Stable release for R 2.0.0-2.2.1.  Fixed bugs 
+related to VDC GUI.  First level dependencies are the same as in version 2.5-1.  
+
+\item {\bf 2.5-2} (February 3, 2006): Stable release for R 2.0.0-2.2.1.  Fixed bugs 
+related to VDC GUI.  First level dependencies are the same as in version 2.5-1.
+
+\item {\bf 2.5-1} (January 31, 2006): Stable release 
+for R 2.0.0-2.2.1.  Added methods for multiple equation models.  Added
+tobit regression.  Fixed bugs related to robust estimation and upgrade of sandwich and 
+zoo packages.  Revised {\tt setx()} to use environments.  Added 
+{\tt current.packages()} to retrieve version of packages upon which Zelig 
+depends.  First level version compatibilities (at time of release) 
+are as follows: \newline
+\begin{tabular}{ll}
+     MASS       & 7.2-24 \\
+     boot       & 1.2-24 \\
+     VGAM       & 0.6-7 \\
+     mvtnorm    & 0.7-2 \\ 
+     survival   & 2.20  \\
+     sandwich   & 1.1-0 \\
+     zoo        & 1.0-4 \\
+     MCMCpack   & 0.6-6 \\
+     coda       & 0.10-3 \\
+\end{tabular}
+
+\item \textbf{2.4-7} (December 10, 2005): Stable release for R 
+2.0.0-2.2.2.  Fixed the environment of {\tt eval()} called within {\tt
+  setx.default()} (thanks to Micah Altman).
+
+\item \textbf{2.4-6} (October 27, 2005): Stable release for R 
+2.0.0-2.2.2.  Fixed bug related to simulation for Bayesian Normal regression.
+
+\item \textbf{2.4-5} (October 18, 2005): Stable release for R 2.0.0-2.2.0.  
+Fixed installation instructions.  
+
+\item \textbf{2.4-4} (September 29, 2005): Stable release for R 
+2.0.0-2.2.0.  Fixed {\tt help.zelig()} links.  
+
+\item \textbf{2.4-3} (September 29, 2005): Stable release for R
+2.0.0-2.2.0.  Revised {\tt matchit()} documentation.  
+
+\item \textbf{2.4-2} (August 30, 2005): Stable release for R 2.0.0-2.1.1.  
+  Fixed bug in {\tt setx()} related to {\tt as.factor()} and {\tt I()}.  
+Streamlined {\tt qi.survreg()}.  
+
+\item \textbf{2.4-1} (August 15, 2005): Stable release for R 2.0.0-2.1.1.  Added the
+  following Bayesian models: factor analysis, mixed factor analysis,
+  ordinal factor analysis, unidimensional item response theory,
+  k-dimensional item response theory, logit, multinomial logit,
+  normal, ordinal probit, Poisson, and tobit.  Also fixed minor bug in
+formula (long variable names coerced to list).  
+
+\item \textbf{2.3-2} (August 5, 2005): Stable release for R 2.0.0-2.1.1.  
+Fixed bug in simulation procedure for lognormal model. 
+
+\item \textbf{2.3-1} (August 4, 2005): Stable release for R 2.0.0-2.1.1.  
+Fixed documentation errors related to model parameterization and code bugs 
+related to first differences and conditional prediction for exponential, 
+lognormal, and Weibull models.  (reported by Alison Post)
+
+\item \textbf{2.2-4} (July 30, 2005):  Stable release for R
+2.0.0-2.1.1.  Revised relogit, adding option for weighting in addition
+to prior correction.  (reported by Martin Pl\"oderl)
+
+\item \textbf{2.2-3} (July 24, 2005): Stable release for R 2.0.0-2.1.1.  
+Fixed bug associated with robust standard errors for negative binomial.  
+
+\item \textbf{2.2-2} (July 13, 2005): Stable release for R 2.0.0-2.1.1.  
+Fixed bug in setx().  (reported by Ying Lu)
+
+\item \textbf{2.2-1} (July 11, 2005):  Stable release for R 2.0.0-2.1.0.  
+Revised ordinal probit to use MASS library.  Added robust standard errors 
+for the following regression models: exponential, gamma, logit, lognormal, 
+least squares, negative binomial, normal (Gaussian), poisson, probit, and 
+weibull.
+
+\item \textbf{2.1-4} (May 22, 2005):  Stable release for R 1.9.1-2.1.0.  Revised help.zelig()
+  to deal with CRAN build of Windows version.  Added recode of slots to lists    
+  in NAMESPACE.  Revised {\tt install.R} script to deal with changes to {\tt install.packages()}.  (reported by Dan Powers and 
+Ying Lu)
+
+\item \textbf{2.1-3} (May 9, 2005):  Stable release for R 1.9.1-2.1.0.  
+Revised param.lm() function to work with bootstrap simulation.  (reported 
+by Jens Hainmueller)
+
+\item \textbf{2.1-2} (April 14, 2005):  Stable release for R 1.9.1-2.1.0.  
+Revised summary.zelig(). 
+\item \textbf{2.1-1} (April 7, 2005):  Stable release for R 1.9.1-2.1.0.  
+Fixed bugs in 
+  NAMESPACE and summary.vglm().
+\item \textbf{2.0-14} (April 5, 2005): Stable release for R
+  1.9.1-2.0.1. Added {\tt summary.vglm()} to ensure the compatibility with VGAM 0.6-2.
+\item \textbf{2.0-13} (March 11, 2005): Stable release for R 1.9.1-2.0.1.  
+Fixed bugs in {\tt NAMESPACE} and R-help file for {\tt rocplot()}.  
+\item \textbf{2.0-12} (February 20, 2005): Stable release for R 
+1.9.1-2.0.1.  Added {\tt plot = TRUE} option to {\tt rocplot()}.  
+\item \textbf{2.0-11} (January 14, 2005): Stable release for R 1.9.1-2.0.1.  
+Changed class name for subsettted models from {\tt "multiple"} to {\tt 
+"strata"}, and modified affected functions.  
+\item \textbf{2.0-10} (January 5, 2005): Stable release for R 1.9.1 and R 
+2.0.0.  Fixed bug in ordinal logit simulation procedure.  (reported by Ian 
+Yohai)
+\item \textbf{2.0-9} (October 21, 2004): Stable release for R 1.9.1 
+\emph{and} R 2.0.0 (Linux and Windows).  Fixed bug in NAMESPACE file.  
+\item \textbf{2.0-8} (October 18, 2004): Stable release for R 1.9.1 
+\emph{and} R 2.0.0 (Linux only).  Revised for submission to CRAN.  
+\item \textbf{2.0-7} (October 14, 2004): Stable release for R 1.9.1 \emph{and} R 2.0.0 (Linux 
+only).  Fixed bugs in {\tt summary.zelig()},  NAMESPACE, and assorted bugs related to new R 
+release.  Revised syntax for multiple equation models.  
+\item \textbf{2.0-6} (October 4, 2004): Stable release for R 1.9.1.
+Fixed problem with NAMESPACE.  
+\item \textbf{2.0-5} (September 25, 2004): Stable release for R 1.9.1.  
+Changed installation procedure to source {\tt install.R} from Zelig 
+website.
+\item \textbf{2.0-4} (September 22, 2004): Stable release for R 1.9.1.  Fixed 
+typo in installation directions, implemented NAMESPACE, rationalized {\tt 
+summary.zelig()}, and tweaked documentation for least squares.  
+\item \textbf{2.0-3} (September 1, 2004): Stable release for 
+R 1.9.1.  Fixed bug in conditional prediction for survival models.  
+\item \textbf{2.0-2} (August 25, 2004): Stable release for R 1.9.1.  
+Removed predicted values from {\tt ls}.  
+\item \textbf{2.0-1b} (July 16, 2004): Stable release for R 1.9.1.  MD5
+  checksum problem fixed.  Revised {\tt plot.zelig()} command to be a
+  generic function with methods assigned by the model.  Revised entire
+  architecture to accept multiply imputed data sets with strata.
+  Added functions to simplify adding models.  Completely restructured
+  reference manual.  Fixed bugs related to conditional prediction in
+  setx and summarizing strata in summary.zelig.
+ \item \textbf{1.1-2} (June 24, 2004): Stable release for R 1.9.1 (MD5
+  checksum problem not fixed, but does not seem to cause problems).
+  Fixed bug in {\tt help.zelig()}.  (reported by Michael L. Levitan)
+\item \textbf{1.1-1} (June 14, 2004): Stable release for R 1.9.0.
+  Revised {\tt zelig()} procedure to use {\tt zelig2model()} wrappers,
+  revised {\tt help.zelig()} to use a data file with extension {\tt
+    .url.tab}, and revised {\tt setx()} procedure to take a list of
+  {\tt fn} to apply to variables, and such that {\tt fn = NULL}
+  returns the entire {\tt model.matrix()}.
+ \item \textbf{1.0-8} (May 27, 2004): Stable release for R 1.9.0.
+   Fixed bug in simulation procedure for survival models.  (reported
+   by Elizabeth Stuart) 
+ \item \textbf{1.0-7} (May 26, 2004): Stable release for R 1.9.0. Fixed 
+   bug in relogit simulation procedure.  (reported by Tom Vanwellingham)
+ \item \textbf{1.0-6} (May 11, 2004):  Stable release for R 1.9.0.  
+   Fixed bug in setx.default, which had previously failed to ignore 
+   extraneous variables in data frame.  (reported by Steve Purpura) 
+ \item \textbf{1.0-5} (May 7, 2004): Replaced relogit procedure with 
+   memory-efficient version. (reported by Tom Vanwellingham)
+ \item \textbf{1.0-4} (April 19, 2004):  Stable release for R 1.9.0.  
+   Added vcov.lm method; changed print for summary.relogit. 
+ \item \textbf{1.0-2} (April 16, 2004): Testing distribution for R 1.9.0. 
+ \item \textbf{1.0-1} (March, 23, 2004): Stable release for R 1.8.1. 
+\end{itemize}
+
+\section{What's Next?}
+
+We have several plans for expanding and improving Zelig.  Major
+changes slated for Version 3.0 (and beyond) include:  
+
+\begin{itemize}
+\item Hierarchical and multi-level models
+\item Ecological inference models
+\item GEE models
+\item Neural network models
+\item Average treatment effects for everyone (treated and control
+units)
+\item Cross-validation
+\item Time-series cross-sectional models (via {\tt nlme})
+\item Generalized boosted regression model (via {\tt gbm})
+\item Saving random seeds to ensure exact replication
+\end{itemize}
+
+If you have suggestions, or packages that you would like to contribute
+to Zelig, please email our listserv at \hlink{zelig at latte.harvard.edu}{mailto:zelig at latte.harvard.edu}
+
+\bibliographystyle{asa}
+\bibliography{gk,gkpubs}
+
+\end{document}
+
+%%% Local Variables: 
+%%% mode: LaTeX
+%%% TeX-master: t
+%%% End:
+
diff --git a/inst/doc/zelig.toc b/inst/doc/zelig.toc
new file mode 100644
index 0000000..89ece59
--- /dev/null
+++ b/inst/doc/zelig.toc
@@ -0,0 +1,544 @@
+\contentsline {chapter}{\numberline {1}Introduction}{6}{chapter.1}
+\contentsline {section}{\numberline {1.1}What Zelig and R Do}{6}{section.1.1}
+\contentsline {section}{\numberline {1.2}Getting Help}{7}{section.1.2}
+\contentsline {part}{I\hspace {1em}User's Guide}{9}{part.1}
+\contentsline {chapter}{\numberline {2}Installation}{10}{chapter.2}
+\contentsline {subsubsection}{If You Know R}{10}{section*.3}
+\contentsline {subsubsection}{If You Are New to R}{10}{section*.4}
+\contentsline {section}{\numberline {2.1}Windows}{11}{section.2.1}
+\contentsline {subsubsection}{Installing R}{11}{section*.5}
+\contentsline {subsubsection}{Installing Zelig}{11}{section*.6}
+\contentsline {subsubsection}{Updating Zelig}{12}{section*.7}
+\contentsline {section}{\numberline {2.2}MacOS X}{12}{section.2.2}
+\contentsline {subsubsection}{Installing R}{12}{section*.8}
+\contentsline {subsubsection}{Installing Zelig}{12}{section*.9}
+\contentsline {subsubsection}{Updating Zelig}{14}{section*.10}
+\contentsline {section}{\numberline {2.3}UNIX and Linux}{15}{section.2.3}
+\contentsline {subsubsection}{Installing R}{15}{section*.11}
+\contentsline {subsubsection}{Installing Zelig}{15}{section*.12}
+\contentsline {subsubsection}{Updating Zelig}{16}{section*.13}
+\contentsline {section}{\numberline {2.4}Version Compatability}{17}{section.2.4}
+\contentsline {chapter}{\numberline {3}Data Analysis Commands}{18}{chapter.3}
+\contentsline {section}{\numberline {3.1}Command Syntax}{18}{section.3.1}
+\contentsline {subsection}{\numberline {3.1.1}Getting Started}{18}{subsection.3.1.1}
+\contentsline {subsection}{\numberline {3.1.2}Details}{19}{subsection.3.1.2}
+\contentsline {section}{\numberline {3.2}Data Sets}{20}{section.3.2}
+\contentsline {subsection}{\numberline {3.2.1}Data Structures}{20}{subsection.3.2.1}
+\contentsline {subsection}{\numberline {3.2.2}Loading Data}{20}{subsection.3.2.2}
+\contentsline {subsection}{\numberline {3.2.3}Saving Data}{22}{subsection.3.2.3}
+\contentsline {section}{\numberline {3.3}Variables}{23}{section.3.3}
+\contentsline {subsection}{\numberline {3.3.1}Classes of Variables}{23}{subsection.3.3.1}
+\contentsline {subsection}{\numberline {3.3.2}Recoding Variables}{24}{subsection.3.3.2}
+\contentsline {chapter}{\numberline {4}Statistical Commands}{29}{chapter.4}
+\contentsline {section}{\numberline {4.1}Zelig Commands}{29}{section.4.1}
+\contentsline {subsection}{\numberline {4.1.1}Quick Overview}{29}{subsection.4.1.1}
+\contentsline {subsection}{\numberline {4.1.2}Examples}{30}{subsection.4.1.2}
+\contentsline {subsection}{\numberline {4.1.3}Details}{32}{subsection.4.1.3}
+\contentsline {section}{\numberline {4.2}Supported Models}{37}{section.4.2}
+\contentsline {section}{\numberline {4.3}Replication Procedures}{40}{section.4.3}
+\contentsline {subsection}{\numberline {4.3.1}Saving Replication Materials}{40}{subsection.4.3.1}
+\contentsline {subsection}{\numberline {4.3.2}Replicating Analyses}{41}{subsection.4.3.2}
+\contentsline {chapter}{\numberline {5}Graphing Commands}{43}{chapter.5}
+\contentsline {section}{\numberline {5.1}Drawing Plots}{43}{section.5.1}
+\contentsline {section}{\numberline {5.2}Adding Points, Lines, and Legends to Existing Plots}{45}{section.5.2}
+\contentsline {section}{\numberline {5.3}Saving Graphs to Files}{45}{section.5.3}
+\contentsline {section}{\numberline {5.4}Examples}{47}{section.5.4}
+\contentsline {subsection}{\numberline {5.4.1}Descriptive Plots: Box-plots}{47}{subsection.5.4.1}
+\contentsline {subsection}{\numberline {5.4.2}Density Plots: A Histogram}{48}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}Advanced Examples}{49}{subsection.5.4.3}
+\contentsline {part}{II\hspace {1em}Advanced Zelig Uses}{52}{part.2}
+\contentsline {chapter}{\numberline {6}R Objects}{53}{chapter.6}
+\contentsline {section}{\numberline {6.1}Scalar Values}{53}{section.6.1}
+\contentsline {section}{\numberline {6.2}Data Structures}{54}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}Arrays}{54}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}Lists}{57}{subsection.6.2.2}
+\contentsline {subsection}{\numberline {6.2.3}Data Frames}{58}{subsection.6.2.3}
+\contentsline {subsection}{\numberline {6.2.4}Identifying Objects and Data Structures}{59}{subsection.6.2.4}
+\contentsline {chapter}{\numberline {7}Programming Statements}{60}{chapter.7}
+\contentsline {section}{\numberline {7.1}Functions}{60}{section.7.1}
+\contentsline {section}{\numberline {7.2}If-Statements}{60}{section.7.2}
+\contentsline {section}{\numberline {7.3}For-Loops}{61}{section.7.3}
+\contentsline {paragraph}{Example 1: Creating a vector with a logical statement}{61}{section*.24}
+\contentsline {paragraph}{Example 2: Creating dummy variables by hand}{62}{section*.25}
+\contentsline {paragraph}{Example 3: Weighted regression with subsets}{63}{section*.26}
+\contentsline {chapter}{\numberline {8}Writing New Models}{65}{chapter.8}
+\contentsline {section}{\numberline {8.1}Managing Statistical Model Inputs}{66}{section.8.1}
+\contentsline {subsection}{\numberline {8.1.1}Describe the Statistical Model}{66}{subsection.8.1.1}
+\contentsline {subsection}{\numberline {8.1.2}Single Response Variable Models: Normal Regression Model}{67}{subsection.8.1.2}
+\contentsline {subsection}{\numberline {8.1.3}Multivariate models: Bivariate Normal example}{70}{subsection.8.1.3}
+\contentsline {section}{\numberline {8.2}Easy Ways to Manage Matrices}{72}{section.8.2}
+\contentsline {subsection}{\numberline {8.2.1}The Intuitive Layout}{73}{subsection.8.2.1}
+\contentsline {subsection}{\numberline {8.2.2}The Computationally-Efficient Layout}{73}{subsection.8.2.2}
+\contentsline {subsection}{\numberline {8.2.3}The Memory-Efficient Layout}{74}{subsection.8.2.3}
+\contentsline {subsection}{\numberline {8.2.4}Interchanging the Three Methods}{74}{subsection.8.2.4}
+\contentsline {chapter}{\numberline {9}Adding Models and Methods to Zelig}{77}{chapter.9}
+\contentsline {section}{\numberline {9.1}Making the Model Compatible with Zelig}{78}{section.9.1}
+\contentsline {subsubsection}{To Work with {\tt zelig()}}{79}{section*.27}
+\contentsline {subsubsection}{To Work with {\tt setx()}}{80}{section*.28}
+\contentsline {subsubsection}{Compatibility with {\tt sim()}}{80}{section*.29}
+\contentsline {paragraph}{Simulating Parameters}{80}{section*.30}
+\contentsline {paragraph}{Calculating Quantities of Interest}{82}{section*.31}
+\contentsline {section}{\numberline {9.2}Getting Ready for the GUI}{84}{section.9.2}
+\contentsline {section}{\numberline {9.3}Formatting Reference Manual Pages}{84}{section.9.3}
+\contentsline {part}{III\hspace {1em}Reference Manual}{87}{part.3}
+\contentsline {chapter}{\numberline {10}Main Commands}{88}{chapter.10}
+\contentsline {section}{\numberline {10.1}{\tt zelig}: Estimating a Statistical Model}{88}{section.10.1}
+\contentsline {subsubsection}{Description}{88}{section*.33}
+\contentsline {subsubsection}{Syntax}{88}{section*.34}
+\contentsline {subsubsection}{Arguments}{88}{section*.35}
+\contentsline {subsubsection}{Output Values}{90}{section*.36}
+\contentsline {subsubsection}{Examples}{90}{section*.37}
+\contentsline {subsubsection}{See Also}{90}{section*.38}
+\contentsline {subsubsection}{Contributors}{91}{section*.39}
+\contentsline {section}{\numberline {10.2}{\tt setx}: Setting Explanatory Variable Values}{92}{section.10.2}
+\contentsline {subsubsection}{Description}{92}{section*.40}
+\contentsline {subsubsection}{Syntax}{92}{section*.41}
+\contentsline {subsubsection}{Arguments}{92}{section*.42}
+\contentsline {subsubsection}{Output Values}{93}{section*.43}
+\contentsline {subsubsection}{Example: Unconditional Prediction}{93}{section*.44}
+\contentsline {subsubsection}{Example: Conditional Prediction With MatchIt Data}{93}{section*.45}
+\contentsline {subsubsection}{Example: Conditional Prediction With Multiple Analyses}{94}{section*.46}
+\contentsline {subsubsection}{See Also}{94}{section*.47}
+\contentsline {subsubsection}{Contributors}{94}{section*.48}
+\contentsline {section}{\numberline {10.3}{\tt sim}: Simulating Quantities of Interest}{95}{section.10.3}
+\contentsline {subsubsection}{Description}{95}{section*.49}
+\contentsline {subsubsection}{Syntax}{95}{section*.50}
+\contentsline {subsubsection}{Arguments}{95}{section*.51}
+\contentsline {subsubsection}{Output Values}{96}{section*.52}
+\contentsline {subsubsection}{See Also}{97}{section*.53}
+\contentsline {subsubsection}{Contributors}{97}{section*.54}
+\contentsline {section}{\numberline {10.4}{\tt summary}: Summarizing Zelig Output}{98}{section.10.4}
+\contentsline {subsubsection}{Description}{98}{section*.55}
+\contentsline {subsubsection}{Syntax}{98}{section*.56}
+\contentsline {subsubsection}{Arguments for multiply-imputed {\tt zelig()} output}{98}{section*.57}
+\contentsline {subsubsection}{Arguments for subsetted {\tt zelig()} output}{98}{section*.58}
+\contentsline {subsubsection}{Arguments for {\tt sim()} output}{99}{section*.59}
+\contentsline {subsubsection}{Output Values}{99}{section*.60}
+\contentsline {subsubsection}{See Also}{99}{section*.61}
+\contentsline {subsubsection}{Contributors}{100}{section*.62}
+\contentsline {section}{\numberline {10.5}{\tt plot}: Graphing Quantities of Interest}{101}{section.10.5}
+\contentsline {subsubsection}{Description}{101}{section*.63}
+\contentsline {subsubsection}{Syntax}{101}{section*.64}
+\contentsline {subsubsection}{Arguments}{101}{section*.65}
+\contentsline {subsubsection}{Output Values}{101}{section*.66}
+\contentsline {subsubsection}{Examples}{101}{section*.67}
+\contentsline {subsubsection}{See Also}{102}{section*.68}
+\contentsline {subsubsection}{Contributors}{102}{section*.69}
+\contentsline {section}{\numberline {10.6}{\tt print}: Printing Quantities of Interest}{103}{section.10.6}
+\contentsline {subsubsection}{Description}{103}{section*.70}
+\contentsline {subsubsection}{Syntax}{103}{section*.71}
+\contentsline {subsubsection}{Arguments}{103}{section*.72}
+\contentsline {subsubsection}{Examples}{103}{section*.73}
+\contentsline {subsubsection}{See Also}{103}{section*.74}
+\contentsline {subsubsection}{Contributors}{103}{section*.75}
+\contentsline {section}{\numberline {10.7}{\tt repl}: Replicating Analyses}{104}{section.10.7}
+\contentsline {subsubsection}{Description}{104}{section*.76}
+\contentsline {subsubsection}{Syntax}{104}{section*.77}
+\contentsline {subsubsection}{Arguments}{104}{section*.78}
+\contentsline {subsubsection}{Output Values}{104}{section*.79}
+\contentsline {subsubsection}{Examples}{105}{section*.80}
+\contentsline {subsubsection}{See Also}{105}{section*.81}
+\contentsline {subsubsection}{Contributors}{105}{section*.82}
+\contentsline {chapter}{\numberline {11}Supplementary Commands}{106}{chapter.11}
+\contentsline {section}{\numberline {11.1}{\tt matchit}: Create matched data}{106}{section.11.1}
+\contentsline {subsubsection}{Description}{106}{section*.83}
+\contentsline {subsubsection}{Syntax}{106}{section*.84}
+\contentsline {subsubsection}{Arguments}{106}{section*.85}
+\contentsline {paragraph}{Arguments for All Matching Methods}{106}{section*.86}
+\contentsline {paragraph}{Additional Arguments for Specification of Distance Measures}{107}{section*.87}
+\contentsline {paragraph}{Additional Arguments for Subclassification}{109}{section*.88}
+\contentsline {paragraph}{Additional Arguments for Nearest Neighbor Matching}{109}{section*.89}
+\contentsline {paragraph}{Additional Arguments for Optimal Matching}{110}{section*.90}
+\contentsline {paragraph}{Additional Arguments for Full Matching}{110}{section*.91}
+\contentsline {paragraph}{Additional Arguments for Genetic Matching}{110}{section*.92}
+\contentsline {subsubsection}{Output Values}{111}{section*.93}
+\contentsline {subsubsection}{Contributors}{112}{section*.94}
+\contentsline {section}{\numberline {11.2}{\tt mi}: Create a list of multiply imputed data frames}{113}{section.11.2}
+\contentsline {subsubsection}{Description}{113}{section*.95}
+\contentsline {subsubsection}{Syntax}{113}{section*.96}
+\contentsline {subsubsection}{Arguments}{113}{section*.97}
+\contentsline {subsubsection}{Output Values}{113}{section*.98}
+\contentsline {subsubsection}{See Also}{113}{section*.99}
+\contentsline {subsubsection}{Contributors}{113}{section*.100}
+\contentsline {section}{\numberline {11.3}{\tt plot.ci}: Plotting Vertical confidence Intervals}{114}{section.11.3}
+\contentsline {subsubsection}{Description}{114}{section*.101}
+\contentsline {subsubsection}{Syntax}{114}{section*.102}
+\contentsline {subsubsection}{Arguments}{114}{section*.103}
+\contentsline {subsubsection}{Output Values}{114}{section*.104}
+\contentsline {subsubsection}{Example}{114}{section*.105}
+\contentsline {subsubsection}{See Also}{115}{section*.106}
+\contentsline {subsubsection}{Contributors}{115}{section*.107}
+\contentsline {section}{\numberline {11.4}{\tt rocplot}: Receiver Operator Characteristic Plots}{116}{section.11.4}
+\contentsline {subsubsection}{Description}{116}{section*.108}
+\contentsline {subsubsection}{Syntax}{116}{section*.109}
+\contentsline {subsubsection}{Arguments}{116}{section*.110}
+\contentsline {subsubsection}{Output Values}{117}{section*.111}
+\contentsline {subsubsection}{Example}{117}{section*.112}
+\contentsline {subsubsection}{See Also}{117}{section*.113}
+\contentsline {subsubsection}{Contributors}{117}{section*.114}
+\contentsline {section}{\numberline {11.5}{\tt ternaryplot}: Ternary Diagram for 3D Data}{118}{section.11.5}
+\contentsline {subsubsection}{Description}{118}{section*.115}
+\contentsline {subsubsection}{Syntax}{118}{section*.116}
+\contentsline {subsubsection}{Arguments}{118}{section*.117}
+\contentsline {subsubsection}{Examples}{119}{section*.118}
+\contentsline {subsubsection}{See Also}{119}{section*.119}
+\contentsline {subsubsection}{Contributors}{119}{section*.120}
+\contentsline {section}{\numberline {11.6}{\tt ternarypoints}: Adding Points to Ternary Diagrams}{120}{section.11.6}
+\contentsline {subsubsection}{Description}{120}{section*.121}
+\contentsline {subsubsection}{Syntax}{120}{section*.122}
+\contentsline {subsubsection}{Arguments}{120}{section*.123}
+\contentsline {subsubsection}{Output Values}{120}{section*.124}
+\contentsline {subsubsection}{Examples}{120}{section*.125}
+\contentsline {subsubsection}{See Also}{120}{section*.126}
+\contentsline {subsubsection}{Contributors}{120}{section*.127}
+\contentsline {chapter}{\numberline {12}Models Zelig Can Run}{121}{chapter.12}
+\contentsline {section}{\numberline {12.1}{\tt arima}: {\sc arima} Models for Time Series Data}{123}{section.12.1}
+\contentsline {paragraph}{Example 1: No External Regressors}{125}{section*.135}
+\contentsline {paragraph}{Example 2: External Regressors, 1 Counterfactual, 1 Time Period}{125}{section*.136}
+\contentsline {paragraph}{Example 3: External Regressors, Counterfactuals Over Many Time Periods}{126}{section*.137}
+\contentsline {section}{\numberline {12.2}{\tt blogit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{129}{section.12.2}
+\contentsline {subsubsection}{Syntax}{129}{section*.142}
+\contentsline {subsubsection}{Input Values}{129}{section*.143}
+\contentsline {subsubsection}{Examples}{129}{section*.144}
+\contentsline {subsubsection}{Model}{131}{section*.145}
+\contentsline {subsubsection}{Quantities of Interest}{132}{section*.146}
+\contentsline {subsubsection}{Output Values}{133}{section*.147}
+\contentsline {subsubsection}{Contributors}{134}{section*.148}
+\contentsline {section}{\numberline {12.3}{\tt bprobit}: Bivariate Logistic Regression for Two Dichotomous Dependent Variables}{135}{section.12.3}
+\contentsline {subsubsection}{Syntax}{135}{section*.149}
+\contentsline {subsubsection}{Input Values}{135}{section*.150}
+\contentsline {subsubsection}{Examples}{136}{section*.151}
+\contentsline {subsubsection}{Model}{137}{section*.152}
+\contentsline {subsubsection}{Quantities of Interest}{138}{section*.153}
+\contentsline {subsubsection}{Output Values}{139}{section*.154}
+\contentsline {subsubsection}{Contributors}{140}{section*.155}
+\contentsline {section}{\numberline {12.4}\texttt {ei.dynamic}: Quinn's Dynamic Ecological Inference Model}{142}{section.12.4}
+\contentsline {subsubsection}{Syntax}{142}{section*.156}
+\contentsline {subsubsection}{Inputs}{142}{section*.157}
+\contentsline {subsubsection}{Additional Inputs}{142}{section*.158}
+\contentsline {subsubsection}{Convergence}{143}{section*.159}
+\contentsline {subsubsection}{Examples}{144}{section*.160}
+\contentsline {subsubsection}{Model}{145}{section*.161}
+\contentsline {subsubsection}{Output Values}{146}{section*.162}
+\contentsline {subsubsection}{Contributors}{147}{section*.163}
+\contentsline {section}{\numberline {12.5}\texttt {ei.hier}: Hierarchical Ecological Inference Model for $2 \times 2$ Tables}{148}{section.12.5}
+\contentsline {subsubsection}{Syntax}{148}{section*.164}
+\contentsline {subsubsection}{Inputs}{148}{section*.165}
+\contentsline {subsubsection}{Additional Inputs}{148}{section*.166}
+\contentsline {subsubsection}{Convergence}{149}{section*.167}
+\contentsline {subsubsection}{Examples}{150}{section*.168}
+\contentsline {subsubsection}{Model}{151}{section*.169}
+\contentsline {subsubsection}{Output Values}{152}{section*.170}
+\contentsline {subsubsection}{Contributors}{153}{section*.171}
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+\contentsline {chapter}{\numberline {A}Frequently Asked Questions}{317}{appendix.A}
+\contentsline {section}{\numberline {A.1}For All Zelig Users}{317}{section.A.1}
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-<<
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->>
-startxref
-1309255
-%%EOF
diff --git a/man/approval.Rd b/man/approval.Rd
new file mode 100644
index 0000000..e5eeb74
--- /dev/null
+++ b/man/approval.Rd
@@ -0,0 +1,27 @@
+\name{approval}
+\alias{approval}
+
+\title{U.S. Presidential Approval Data}
+
+\description{
+  Monthy public opinion data for 2001-2006.  
+}
+
+\usage{data(approval)}
+
+\format{
+  A table containing 8 variables ("month", "year", "approve", 
+  "disapprove", "unsure", "sept.oct.2001", "iraq.war", and "avg.price")
+  and 65 observations.  }
+
+\source{ICPSR}
+
+\references{
+  Stuff here
+}
+
+\keyword{datasets}
+
+
+
+

-- 
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/r-cran-zelig.git



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