[r-cran-mnp] 49/51: Import Upstream version 2.6-4

Andreas Tille tille at debian.org
Fri Sep 8 14:14:49 UTC 2017


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

commit a084a9063ca5efd9fee46faf6ce260c4af036c26
Author: Andreas Tille <tille at debian.org>
Date:   Fri Sep 8 15:55:26 2017 +0200

    Import Upstream version 2.6-4
---
 DESCRIPTION     |  49 ++++++++++++++++++++++++++-----------------------
 MD5             |  10 +++++-----
 MNP.pdf         | Bin 3497615 -> 3497571 bytes
 R/mnp.R         |   4 ++--
 R/onAttach.R    |   7 ++++---
 R/xmatrix.mnp.R |   2 +-
 6 files changed, 38 insertions(+), 34 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d031897..4fc7f12 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,32 +1,35 @@
 Package: MNP
-Version: 2.6-3
-Date: 2011-12-06
+Version: 2.6-4
+Date: 2013-06-09
 Title: R Package for Fitting the Multinomial Probit Model
-Author: Kosuke Imai <kimai at princeton.edu>, 
-        David A. van Dyk <dvd at uci.edu>. 
+Author: Kosuke Imai <kimai at princeton.edu>, David A. van Dyk
+        <dvd at uci.edu>.
 Maintainer: Kosuke Imai <kimai at princeton.edu>
 Depends: R (>= 2.1), MASS, utils
-Description: MNP is a publicly available R package that fits the Bayesian
-  multinomial probit model via Markov chain Monte Carlo. The
-  multinomial probit model is often used to analyze the discrete
-  choices made by individuals recorded in survey data. Examples where
-  the multinomial probit model may be useful include the analysis of
-  product choice by consumers in market research and the analysis of
-  candidate or party choice by voters in electoral studies.  The MNP
-  software can also fit the model with different choice sets for each
-  individual, and complete or partial individual choice orderings of
-  the available alternatives from the choice set. The estimation
-  is based on the efficient marginal data augmentation algorithm that
-  is developed by Imai and van Dyk (2005). ``A Bayesian Analysis of
-  the Multinomial Probit Model Using the Data Augmentation,'' Journal
-  of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. Detailed
-  examples are given in Imai and van Dyk (2005). ``MNP: R Package for 
-  Fitting the Multinomial Probit Model.'' Journal of Statistical Software,
-  Vol. 14, No. 3 (May), pp. 1-32.
+Description: MNP is a publicly available R package that fits the
+        Bayesian multinomial probit model via Markov chain Monte Carlo.
+        The multinomial probit model is often used to analyze the
+        discrete choices made by individuals recorded in survey data.
+        Examples where the multinomial probit model may be useful
+        include the analysis of product choice by consumers in market
+        research and the analysis of candidate or party choice by
+        voters in electoral studies.  The MNP software can also fit the
+        model with different choice sets for each individual, and
+        complete or partial individual choice orderings of the
+        available alternatives from the choice set. The estimation is
+        based on the efficient marginal data augmentation algorithm
+        that is developed by Imai and van Dyk (2005). ``A Bayesian
+        Analysis of the Multinomial Probit Model Using the Data
+        Augmentation,'' Journal of Econometrics, Vol. 124, No. 2
+        (February), pp. 311-334. Detailed examples are given in Imai
+        and van Dyk (2005). ``MNP: R Package for Fitting the
+        Multinomial Probit Model.'' Journal of Statistical Software,
+        Vol. 14, No. 3 (May), pp. 1-32.
 LazyLoad: yes
 LazyData: yes
 License: GPL (>= 2)
 URL: http://imai.princeton.edu/software/MNP.html
-Packaged: 2011-12-07 01:33:35 UTC; kimai
+Packaged: 2013-06-09 17:45:34 UTC; kimai
+NeedsCompilation: yes
 Repository: CRAN
-Date/Publication: 2011-12-07 10:13:42
+Date/Publication: 2013-06-09 20:16:42
diff --git a/MD5 b/MD5
index 1b6b0a4..c97bfc4 100644
--- a/MD5
+++ b/MD5
@@ -1,15 +1,15 @@
-df4b5179b39eb6c39808e112de25d26f *DESCRIPTION
-9b26f9253e17855e62ec571ecf8dc379 *MNP.pdf
+7c923c417c32a5d1a9b62db575670762 *DESCRIPTION
+e5dbaa21eb5db71bef6d103cc66a5896 *MNP.pdf
 d30ea84d5abbc3f5323c8f384954ee69 *NAMESPACE
 97a7c7aa155788b4a62bdd3e3c02fab0 *R/coef.mnp.R
 127b786d97aa185eac38721c23aa2f90 *R/cov.mnp.R
-3a6ed5aa696b22d8c19fc5071bf51554 *R/mnp.R
-5c809a499b486e0bd0a152b1d313814d *R/onAttach.R
+3f7ce384c42415e3e5fe81e7c74143aa *R/mnp.R
+5e40e20eb515081275508f808be52c3e *R/onAttach.R
 88edaa6559267b5e85bc8bba9b291b97 *R/predict.mnp.R
 e822d2e847f5daf2922a019fa2a4c491 *R/print.mnp.R
 a6f93ff28505296a3b6ed5c67a0286ab *R/print.summary.mnp.R
 a954cf57cf2e252b7837ac4739d87eab *R/summary.mnp.R
-67068018b650fa86614517cf94c81d2f *R/xmatrix.mnp.R
+cfdc76deb4c1e71bdb9422bf9f2cecd9 *R/xmatrix.mnp.R
 f594a4e2db80f456c9dee49d5f9efa6c *R/ymatrix.mnp.R
 c342e6c52a792ec58aac9efa0be703ae *data/detergent.txt.gz
 bb1181c0a078518436a2a7c9ee5594a9 *data/japan.txt.gz
diff --git a/MNP.pdf b/MNP.pdf
index 764fbd0..1868b5a 100644
Binary files a/MNP.pdf and b/MNP.pdf differ
diff --git a/R/mnp.R b/R/mnp.R
index 545f12d..c924c2f 100644
--- a/R/mnp.R
+++ b/R/mnp.R
@@ -4,7 +4,7 @@ mnp <- function(formula, data = parent.frame(), choiceX = NULL,
                 p.df = n.dim+1, p.scale = 1, coef.start = 0,
                 cov.start = 1, burnin = 0, thin = 0, verbose = FALSE) {   
   call <- match.call()
-  mf <- match.call(expand = FALSE)
+  mf <- match.call(expand.dots = FALSE)
   mf$choiceX <- mf$cXnames <- mf$base <- mf$n.draws <- mf$latent <-
     mf$p.var <- mf$p.df <- mf$p.scale <- mf$coef.start <- mf$invcdf <-
       mf$trace <- mf$cov.start <- mf$verbose <- mf$burnin <- mf$thin <- NULL   
@@ -164,7 +164,7 @@ mnp <- function(formula, data = parent.frame(), choiceX = NULL,
   if (latent) {
     W <- array(as.vector(t(param[,(n.par-n.dim*n.obs+1):n.par])),
                dim = c(n.dim, n.obs, floor((n.draws-burnin)/keep)),
-               dimnames = list(lev[-1], rownames(Y), NULL))
+               dimnames = list(lev[!(lev %in% base)], rownames(Y), NULL))
     param <- param[,1:(n.par-n.dim*n.obs)]
     }
   else
diff --git a/R/onAttach.R b/R/onAttach.R
index 84aff8f..e21b04a 100644
--- a/R/onAttach.R
+++ b/R/onAttach.R
@@ -1,6 +1,7 @@
 ".onAttach" <- function(lib, pkg) {
   mylib <- dirname(system.file(package = pkg))
-  title <- packageDescription(pkg, lib = mylib)$Title
-  ver <- packageDescription(pkg, lib = mylib)$Version
-  packageStartupMessage(paste(pkg, ": ", title, "\nVersion: ", ver, "\n", sep=""))
+  title <- packageDescription(pkg, lib.loc = mylib)$Title
+  ver <- packageDescription(pkg, lib.loc = mylib)$Version
+  author <- packageDescription(pkg, lib.loc = mylib)$Author
+  packageStartupMessage(pkg, ": ", title, "\nVersion: ", ver, "\nAuthors: ", author, "\n")
 }
diff --git a/R/xmatrix.mnp.R b/R/xmatrix.mnp.R
index 9760ddd..53d6970 100644
--- a/R/xmatrix.mnp.R
+++ b/R/xmatrix.mnp.R
@@ -2,7 +2,7 @@ xmatrix.mnp <- function(formula, data = parent.frame(), choiceX=NULL,
                         cXnames=NULL, base=NULL, n.dim, lev,
                         MoP=FALSE, verbose=FALSE, extra=FALSE) {
   call <- match.call()
-  mf <- match.call(expand = FALSE)
+  mf <- match.call(expand.dots = FALSE)
   mf$choiceX <- mf$cXnames <- mf$base <- mf$n.dim <- mf$lev <-
     mf$MoP <- mf$verbose <- mf$extra <- NULL  
   

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