[r-cran-gam] 12/20: Imported Upstream version 1.12
Andreas Tille
tille at debian.org
Fri Dec 16 14:32:11 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-gam.
commit ba7eb83a424394ce0bfbe294ef4f66085a31be84
Author: Andreas Tille <tille at debian.org>
Date: Fri Dec 16 13:32:22 2016 +0100
Imported Upstream version 1.12
---
DESCRIPTION | 12 ++++++------
MD5 | 39 ++++++++++++++++++---------------------
NAMESPACE | 12 ++++++++++--
R/anova.gam.R | 7 +++----
R/anova.gamlist.R | 4 ++--
R/deviance.default.R | 3 ---
R/deviance.glm.R | 3 ---
R/deviance.lm.R | 4 ----
R/gam.R | 2 +-
R/gam.fit.R | 4 ++--
R/{all.wam.R => general.wam.R} | 4 ++--
R/gplot.matrix.R | 14 +++++++++++---
R/s.wam.R | 3 +++
R/step.gam.R | 13 +++++++++----
R/summary.gam.R | 5 +++--
man/gam-internal.Rd | 5 +----
man/gam.Rd | 6 ++++--
man/gam.exact.Rd | 7 ++++---
man/lo.Rd | 4 ++--
man/s.Rd | 4 ++--
man/step.gam.Rd | 2 +-
src/Makevars.win | 2 +-
22 files changed, 85 insertions(+), 74 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 5a23881..138210e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,18 +1,18 @@
Package: gam
Type: Package
Title: Generalized Additive Models
-Date: 2013-08-11
-Version: 1.09.1
+Date: 2015-05-11
+Version: 1.12
Author: Trevor Hastie
Description: Functions for fitting and working with generalized
additive models, as described in chapter 7 of "Statistical Models in
S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
Models" (Hastie and Tibshirani, 1990).
Maintainer: Trevor Hastie <hastie at stanford.edu>
-Depends: stats, splines, R (>= 3.1)
-Suggests: akima, foreach
+Depends: stats, splines, foreach
+Suggests: akima
License: GPL-2
-Packaged: 2013-09-12 10:44:42 UTC; ripley
NeedsCompilation: yes
+Packaged: 2015-05-13 05:57:11 UTC; hastie
Repository: CRAN
-Date/Publication: 2014-04-10 10:54:04
+Date/Publication: 2015-05-13 08:49:49
diff --git a/MD5 b/MD5
index 2c2170d..eb1c932 100644
--- a/MD5
+++ b/MD5
@@ -1,20 +1,16 @@
c95bed4e0f0a9c7ce468d8b0c3b86c99 *ChangeLog
-fcc9f03b2aab1a5c90ae3d4439c059b6 *DESCRIPTION
+b30ae6370f11ae1cf2cd18952809b7a7 *DESCRIPTION
af77f82fb0aa5e383808c5f36aa47066 *INDEX
-79c3de2f467638359b6523bce15d127e *NAMESPACE
-3f56ee7eddd13ec792f7c2150b1e1eca *R/all.wam.R
-81e9b54a2bfe88cb19390c5edc0458dd *R/anova.gam.R
-1c6ad753c650b8a285a3f2acff721480 *R/anova.gamlist.R
+e8786be90e351f6e1f9c7bc9b853176d *NAMESPACE
+f2cdce15d203d42d6ea559059e7f5820 *R/anova.gam.R
+cb6e56d47338bc65c4f35ca2b45b8968 *R/anova.gamlist.R
c67ddb150e807c4a0ef77acf132c1021 *R/as.anova.R
996bcecc62a9687cb77863e45830a000 *R/as.data.frame.lo.smooth.R
69d4bccf3afc7a1a9fd2b5d8b2f60fec *R/assign.list.R
-24f8cb3be5bf4bc1739a45087826e78f *R/deviance.default.R
-0d38ab9b7f2639649afa27ee3ac9ffb6 *R/deviance.glm.R
-4ab8260f7666ffd65e9ef915fb245531 *R/deviance.lm.R
-60657ce607548e744477b5b157776116 *R/gam.R
+155300acde3149437b0f3650a1772273 *R/gam.R
6f9d6d8a11d7b20d791233f1ed3445dc *R/gam.control.R
2c633d79441282175358f1a0c148bd50 *R/gam.exact.R
-52ac1d379ebc2189d982e3f86e62c8af *R/gam.fit.R
+363a8ceb73c42777fa19e1741561b6cd *R/gam.fit.R
bd96ac15ba688c863f09f0e632a411f2 *R/gam.lo.R
946d0c85f06753f768700803d197c247 *R/gam.match.R
526f48fabc38d73bef43169182a7051a *R/gam.nlchisq.R
@@ -25,11 +21,12 @@ a6bc1a9e490a60aaa1d5d8d47b872c00 *R/gam.scope.R
1f8d4b4e6776250d0e4080e64a1273e9 *R/gam.sp.R
865f790f7ee2bcf180a2741fba26f7ac *R/gam.wlist.R
3145f500d12b60398d2bf36eb20973c9 *R/gamlist.R
+48b81ce1290eed4ff84af845b760a911 *R/general.wam.R
6bc9b975aa99176b8d021818760af91c *R/gplot.R
f8026ded9300952fe6a9810e8bfeecdf *R/gplot.default.R
55d310d740e82e36a69686600b331072 *R/gplot.factor.R
4e843123d1fd91f5d2fea87e487f795d *R/gplot.list.R
-6f9f511873884b3b16bd995a3ec42086 *R/gplot.matrix.R
+d9e47c37ce11bb5d36eb65e7af17debc *R/gplot.matrix.R
5fc7caa79f441edc92935788f38444b2 *R/gplot.numeric.R
9c06038289b4ccad8383f467b73ad06b *R/labels.gam.R
093d17804fa5ce90f12cdeb4f9fbcf6a *R/lo.R
@@ -48,10 +45,10 @@ ca3a618d4376069f1d43a2aabaea5e4c *R/print.gamex.R
8dbfd56254f091f7d219f5caaeefecc9 *R/print.summary.gam.R
c318b1f9d34fda3e660a9af01b86c830 *R/random.R
1478722abc4269fede0d572840c0cfbe *R/s.R
-596d38e0f6ec11d3921b0ee8bd3590a4 *R/s.wam.R
-79eaf99c6a30f60cdb8d32ad76f24970 *R/step.gam.R
+c0ea33636dd8d8e8088a173fd5670d7f *R/s.wam.R
+564df4d615c924b58ed9168554fa3945 *R/step.gam.R
9c1302d138d07b10f17c27765bd87955 *R/subset.smooth.R
-ed4bf1ea99f4e8e888a4d075c85df6f0 *R/summary.gam.R
+0e3af75530812309d9fb9d4696438046 *R/summary.gam.R
5cc7658080bc925bb5fc11172d78fb41 *R/ylim.scale.R
ba66638e3de17b868b4d98dffe95009d *data/gam.data.RData
83529cbff37939aff8d96d32d6458f12 *data/gam.newdata.RData
@@ -62,21 +59,21 @@ a009bd4d2232ec7cc19b9d7d10280ffb *inst/ratfor/backlo.r
4e0b184dc647e3abac7fb7f023ed4a69 *inst/ratfor/lo.r
58295734adaaee3561f298e7c0b93eee *inst/ratfor/splsm.r
b86dc231f80eb84973ab3ab5f179e423 *man/anova.gam.Rd
-eb542f4c26145749d984010e1dd24213 *man/gam-internal.Rd
-41aa55d9768c1f1fd22c3b1a27b277a6 *man/gam.Rd
+d20a1c1617715af04e1e911a2ce4ba0d *man/gam-internal.Rd
+05847ec1f125a8fe182f6e680d05183c *man/gam.Rd
5167ecd9baaea501d766f87b4e22cf20 *man/gam.control.Rd
d3c27998fb1cdce4cb00703557f1138d *man/gam.data.Rd
-d39920b918d9a57b3ad4090658750e45 *man/gam.exact.Rd
+f317aafb77e4b02b30e81786fb663c5f *man/gam.exact.Rd
fc57a2513d84056abd88a39ac9bc352d *man/gam.scope.Rd
dd3553bd8578858873bd1384b000273a *man/kyphosis.Rd
-800542a98f81e40c0930605f288c9ca4 *man/lo.Rd
+2bac0339dc32adb38b491c7ec1cd8601 *man/lo.Rd
5068084693ec99de54e0531a53d4e637 *man/na.gam.replace.Rd
a79905d34d4d93cb9939329b7cc8f507 *man/plot.gam.Rd
7de080b0a4e684b2a2b4e26e17fe38d8 *man/predict.gam.Rd
-7f2b4a226c564121816e2594f7fd61c3 *man/s.Rd
-824118d9e6900ee9ed48800b17903830 *man/step.gam.Rd
+1825ff921f39de62c26f66960f98da7b *man/s.Rd
+f77c2a626622ba85614fac57d2b896b1 *man/step.gam.Rd
f80de2889856cba0512e5377926af1aa *src/Makevars
-1c81cbb8cbdd19a2e909dc5f802ddb07 *src/Makevars.win
+2fa4c7011c2bc0f7449ae151d5cc44ae *src/Makevars.win
45061d1eb26bda41c4c458126dd303da *src/backfit.f
7205467d48f1b2f05d6b830aa34afac1 *src/backlo.f
10f91c907532cc70e76074ca47d14716 *src/bsplvd.f
diff --git a/NAMESPACE b/NAMESPACE
index ee547a3..8230250 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,7 +1,9 @@
useDynLib(gam)
import(stats)
+import(splines)
+import(foreach)
importFrom(utils,head,tail,packageDescription)
-export(all.wam,anova.gamlist,as.anova,as.data.frame.lo.smooth,assign.list,deviance.default,deviance.glm,deviance.lm,gam,gam.control,gam.exact,gam.fit,gamlist,gam.lo,gam.match,gam.nlchisq,gam.random,gam.s,gam.scope,gam.slist,gam.sp,gam.wlist,gplot,gplot.default,gplot.factor,gplot.list,gplot.matrix,gplot.numeric,lo,lo.wam,na.gam.replace,newdata.predict.gam,plot.gam,polylo,predict.gam,random,s,s.wam,ylim.scale,step.gam,summary.gam)
+export(general.wam,anova.gamlist,as.anova,as.data.frame.lo.smooth,assign.list,gam,gam.control,gam.exact,gam.fit,gamlist,gam.lo,gam.match,gam.nlchisq,gam.random,gam.s,gam.scope,gam.slist,gam.sp,gam.wlist,gplot,gplot.default,gplot.factor,gplot.list,gplot.matrix,gplot.numeric,lo,lo.wam,na.gam.replace,newdata.predict.gam,plot.gam,polylo,predict.gam,random,s,s.wam,ylim.scale,step.gam,summary.gam)
S3method("[",smooth)
S3method(labels,gam)
S3method(plot,gam)
@@ -15,7 +17,13 @@ S3method(plot,preplot.gam)
S3method(plot,gam)
S3method(preplot,gam)
S3method(anova,gam)
-
+S3method(anova,gamlist)
+S3method(as.data.frame,lo.smooth)
+S3method(gplot,default)
+S3method(gplot,factor)
+S3method(gplot,list)
+S3method(gplot,matrix)
+S3method(gplot,numeric)
diff --git a/R/anova.gam.R b/R/anova.gam.R
index 8f736b9..61994b0 100644
--- a/R/anova.gam.R
+++ b/R/anova.gam.R
@@ -4,8 +4,7 @@
test=match.arg(test)
margs <- function(...)
nargs()
- if(margs(...)) {
- anova.glmlist <- getS3method("anova", "glmlist")
- anova.glmlist(list(object, ...), test = test)
- } else summary.gam(object)$anova
+ if(margs(...))
+ anova(structure(list(object, ...),class="glmlist"), test = test)
+ else summary.gam(object)$anova
}
diff --git a/R/anova.gamlist.R b/R/anova.gamlist.R
index 44283c1..2ec6fbe 100644
--- a/R/anova.gamlist.R
+++ b/R/anova.gamlist.R
@@ -1,6 +1,6 @@
"anova.gamlist" <-
function(object, ..., test = c("none", "Chisq", "F", "Cp")){
test=match.arg(test)
- anova(structure(c(list(object), ...), class="glmlist"), test = test)
- ## anova.glmlist(object, test = test)
+ class(object)="glmlist"
+ anova(object, test = test)
}
diff --git a/R/deviance.default.R b/R/deviance.default.R
deleted file mode 100644
index 00fc4d5..0000000
--- a/R/deviance.default.R
+++ /dev/null
@@ -1,3 +0,0 @@
-"deviance.default" <-
-function(object, ...)
-object$deviance
diff --git a/R/deviance.glm.R b/R/deviance.glm.R
deleted file mode 100644
index 6766a19..0000000
--- a/R/deviance.glm.R
+++ /dev/null
@@ -1,3 +0,0 @@
-"deviance.glm" <-
-function(object, ...)
-object$deviance
diff --git a/R/deviance.lm.R b/R/deviance.lm.R
deleted file mode 100644
index 8dac1fe..0000000
--- a/R/deviance.lm.R
+++ /dev/null
@@ -1,4 +0,0 @@
-"deviance.lm" <-
-function(object, ...)
-if(is.null(w <- object$weights)) sum(object$residuals^2.) else sum(w * object$
- residuals^2.)
diff --git a/R/gam.R b/R/gam.R
index 41292f2..a5e22e1 100644
--- a/R/gam.R
+++ b/R/gam.R
@@ -1,7 +1,7 @@
"gam" <-
function(formula, family = gaussian, data,
weights, subset, na.action, start = NULL, etastart, mustart, control = gam.control(...),
- model = FALSE, method="glm.fit", x = FALSE, y = TRUE, ...)
+ model = TRUE, method="glm.fit", x = FALSE, y = TRUE, ...)
{
call <- match.call()
if (is.character(family))
diff --git a/R/gam.fit.R b/R/gam.fit.R
index de93d57..48e679d 100644
--- a/R/gam.fit.R
+++ b/R/gam.fit.R
@@ -76,12 +76,12 @@
assignx <- assign.list(assignx, a$term.labels)
which <- assignx[smooth.labels]
if (length(smoothers) > 1)
- bf <- "all.wam"
+ bf <- "general.wam"
else {
sbf <- match(names(smoothers), gam.wlist, FALSE)
bf <- if (sbf)
paste(gam.wlist[sbf], "wam", sep = ".")
- else "all.wam"
+ else "general.wam"
}
bf.call <- parse(text = paste(bf, "(x, z, wz, fit$smooth, which, fit$smooth.frame,bf.maxit,bf.epsilon, trace)",
sep = ""))[[1]]
diff --git a/R/all.wam.R b/R/general.wam.R
similarity index 97%
rename from R/all.wam.R
rename to R/general.wam.R
index 7956c79..082c73b 100644
--- a/R/all.wam.R
+++ b/R/general.wam.R
@@ -1,4 +1,4 @@
-"all.wam" <-
+"general.wam" <-
function(x, y, w, s, which, smooth.frame, maxit = 30, tol = 1e-7, trace = FALSE,
se = TRUE, ...)
{
@@ -73,7 +73,7 @@
RATIO, ndig)), "\n")
}
if((nit == maxit) & maxit > 1.)
- warning(paste("all.wam convergence not obtained in ", maxit,
+ warning(paste("general.wam convergence not obtained in ", maxit,
" iterations"))
names(df) <- names.calls
if(trace)
diff --git a/R/gplot.matrix.R b/R/gplot.matrix.R
index 61b2fda..a615598 100644
--- a/R/gplot.matrix.R
+++ b/R/gplot.matrix.R
@@ -14,11 +14,19 @@
stop("x must be bivariate")
duplicated(x[, 1] + (1i) * x[, 2])
}
- interp.loaded<-require("akima")
- if(!interp.loaded)stop("You need to install and load the package 'akima' from the R contributed libraries")
+
+
+# interp.loaded<-require("akima")
+ interp.loaded<-TRUE
+# if(!interp.loaded)
xname <- dimnames(x)[[2]]
dups <- bivar.dup(x)
- xyz <- interp(x[!dups, 1], x[!dups, 2], y[!dups])
+ if (requireNamespace("akima", quietly = TRUE)) {
+ xyz <- akima::interp(x[!dups, 1], x[!dups, 2], y[!dups])
+ } else {
+ stop("You need to install the package 'akima' from the R contributed libraries to use this plotting method for bivariate functions")
+ }
+
zmin <- min(xyz$z[!is.na(xyz$z)])
z <- ifelse(is.na(xyz$z), zmin, xyz$z)
scale2 <- diff(range(z))
diff --git a/R/s.wam.R b/R/s.wam.R
index 8cf1cfe..86c450b 100644
--- a/R/s.wam.R
+++ b/R/s.wam.R
@@ -25,6 +25,9 @@ function(x, y, w, s, which, smooth.frame, maxit = 30, tol = 1e-7, trace = FALSE,
storage.mode(w) <- "double"
p <- smooth.frame$p
n <- smooth.frame$n
+### Need to do the signif hack on the which columns of x
+ for(ich in which)x[,ich]=signif(x[,ich],6)
+###
fit <- .Fortran("bakfit",
x,
npetc = as.integer(c(n, p, length(which), se, 0, maxit, 0)),
diff --git a/R/step.gam.R b/R/step.gam.R
index 061a9fb..adc1d62 100644
--- a/R/step.gam.R
+++ b/R/step.gam.R
@@ -6,7 +6,11 @@ step.gam <-
get.visit <- function(trial, visited){
match(paste(trial,collapse=""),apply(visited,2,paste,collapse=""),FALSE)
}
- scope.char <- function(formula) {
+deviancelm <-
+function(object, ...)
+if(is.null(w <- object$weights)) sum(object$residuals^2) else sum(w * object$
+ residuals^2)
+ scope.char <- function(formula) {
formula = update(formula, ~-1 + .)
tt <- terms(formula)
tl <- attr(tt, "term.labels")
@@ -105,10 +109,10 @@ step.gam <-
n <- length(fit$fitted)
if (missing(scale)) {
famname <- family$family["name"]
- scale <- switch(famname, Poisson = 1, Binomial = 1, deviance.lm(fit)/fit$df.resid)
+ scale <- switch(famname, Poisson = 1, Binomial = 1, deviancelm(fit)/fit$df.resid)
}
else if (scale == 0)
- scale <- deviance.lm(fit)/fit$df.resid
+ scale <- deviancelm(fit)/fit$df.resid
bAIC <- fit$aic
if (trace>0)
cat("; AIC=", format(round(bAIC, 4)), "\n")
@@ -153,7 +157,8 @@ step.gam <-
if(is.null(form.list))break
### Now we are ready for the expensive loop
### Parallel is set up
-if(parallel&&require(foreach)){
+#if(parallel&&require(foreach)){
+if(parallel){
# step.list=foreach(i=1:length(form.list),.inorder=FALSE,.packages="gam",.verbose=trace>1)%dopar%
step.list=foreach(i=1:length(form.list),.inorder=FALSE,.verbose=trace>1)%dopar%
{
diff --git a/R/summary.gam.R b/R/summary.gam.R
index e97cf55..85bae44 100644
--- a/R/summary.gam.R
+++ b/R/summary.gam.R
@@ -1,8 +1,9 @@
"summary.gam" <-
function (object, dispersion = NULL, ...)
{
- anova.lm <- getS3method("anova", "lm")
- paod=anova.lm(object,...)
+ object.lm=object
+ class(object.lm)="lm"
+ paod=anova(object.lm,...)
attr(paod,"heading")="Anova for Parametric Effects"
save.na.action <- object$na.action
diff --git a/man/gam-internal.Rd b/man/gam-internal.Rd
index 6f89ca5..25bc279 100644
--- a/man/gam-internal.Rd
+++ b/man/gam-internal.Rd
@@ -2,14 +2,11 @@
\title{Service functions and as yet undocumented functions for the gam library}
\alias{.First.lib}
\alias{[.smooth}
-\alias{all.wam}
+\alias{general.wam}
\alias{anova.gamlist}
\alias{as.anova}
\alias{as.data.frame.lo.smooth}
\alias{assign.list}
-\alias{deviance.default}
-\alias{deviance.glm}
-\alias{deviance.lm}
\alias{gamlist}
\alias{gam.match}
\alias{gam.nlchisq}
diff --git a/man/gam.Rd b/man/gam.Rd
index 462c647..adb4474 100644
--- a/man/gam.Rd
+++ b/man/gam.Rd
@@ -19,7 +19,7 @@
\usage{
gam(formula, family = gaussian, data, weights, subset, na.action,
start, etastart, mustart, control = gam.control(\ldots),
-model=FALSE, method, x=FALSE, y=TRUE, \dots)
+model=TRUE, method, x=FALSE, y=TRUE, \dots)
gam.fit(x, y, smooth.frame, weights = rep(1,nobs), start = NULL,
etastart = NULL, mustart = NULL, offset = rep(0, nobs), family = gaussian(),
@@ -78,7 +78,9 @@ errors; they will simply produce the usual parametric interaction.}
}
\item{model}{a logical value indicating whether \emph{model frame}
- should be included as a component of the returned value.}
+ should be included as a component of the returned value. Needed if
+ \code{gam} is called and predicted from inside a user
+ function. Default is \code{TRUE}.}
\item{method}{the method to be used in fitting the parametric part of
the model.
diff --git a/man/gam.exact.Rd b/man/gam.exact.Rd
index 3f61235..f127e84 100644
--- a/man/gam.exact.Rd
+++ b/man/gam.exact.Rd
@@ -23,10 +23,11 @@ There is a print method for the gamex class.
\emph{JASA}, December 2004, 99(468), 938-948. See
\url{http://www-stat.stanford.edu/~hastie/Papers/dominiciR2.pdf}
}
- \author{Aidan McDermott, Department of Biostatistics, Johns
- Hopkins University. See \url{http://ihapss.biostat.jhsph.edu/software/gam.exact/gam.exact.htm}
+\author{Aidan McDermott, Department of Biostatistics, Johns
+ Hopkins University. See \url{http://www.ihapss.jhsph.edu/software/gam.exact/gam.exact.htm}
Modified by Trevor Hastie for R}
- \examples{
+
+\examples{
set.seed(31)
n <- 200
x <- rnorm(n)
diff --git a/man/lo.Rd b/man/lo.Rd
index 297895b..7917d59 100644
--- a/man/lo.Rd
+++ b/man/lo.Rd
@@ -46,7 +46,7 @@ practice.
The matrix is endowed with a number of attributes; the matrix itself is
used in the construction of the model matrix, while the attributes are
-needed for the backfitting algorithms \code{all.wam} (weighted additive
+needed for the backfitting algorithms \code{general.wam} (weighted additive
model) or \code{lo.wam} (currently not implemented). Local-linear curve
or surface fits reproduce linear responses, while local-quadratic fits
reproduce quadratic curves or surfaces. These parts of the \code{loess}
@@ -65,7 +65,7 @@ of the model.
One important attribute is named \code{call}. For example, \code{lo(x)}
has a call component
\code{gam.lo(data[["lo(x)"]], z, w, span = 0.5, degree = 1, ncols = 1)}.
-This is an expression that gets evaluated repeatedly in \code{all.wam}
+This is an expression that gets evaluated repeatedly in \code{general.wam}
(the backfitting algorithm).
\code{gam.lo} returns an object with components
diff --git a/man/s.Rd b/man/s.Rd
index d5e63a3..b1f2cf9 100644
--- a/man/s.Rd
+++ b/man/s.Rd
@@ -33,7 +33,7 @@ can be used as smoothing parameter, with values typically in
\code{s} returns the vector \code{x}, endowed with a number of
attributes. The vector itself is used in the construction of the model
matrix, while the attributes are needed for the backfitting algorithms
-\code{all.wam} (weighted additive model) or \code{s.wam} (currently not
+\code{general.wam} (weighted additive model) or \code{s.wam} (currently not
implemented). Since smoothing splines reproduces linear fits, the linear
part will be efficiently computed with the other parametric linear parts
of the model.
@@ -44,7 +44,7 @@ for \code{gam}.
One important attribute is named \code{call}. For example, \code{s(x)}
has a call component
\code{gam.s(data[["s(x)"]], z, w, spar = 1, df = 4)}.
-This is an expression that gets evaluated repeatedly in \code{all.wam}
+This is an expression that gets evaluated repeatedly in \code{general.wam}
(the backfitting algorithm).
\code{gam.s} returns an object with components
diff --git a/man/step.gam.Rd b/man/step.gam.Rd
index 6c466c3..184cb3c 100644
--- a/man/step.gam.Rd
+++ b/man/step.gam.Rd
@@ -34,7 +34,7 @@ argument for a large model.
an optional argument used in the definition of the AIC statistic used to evaluate models for selection. By default, the scaled Chi-squared statistic for the initial model is used, but if forward selection is to be performed, this is not necessarily a sound choice.
}
\item{direction}{
-The mode of step-wise search, can be one of \code{"both"}, \code{"backward"}, or \code{"forward"}, with a default of \code{"both"}. If \code{scope} is missing, the default for \code{direction} is "backward".
+The mode of step-wise search, can be one of \code{"both"}, \code{"backward"}, or \code{"forward"}, with a default of \code{"both"}. If \code{scope} is missing, the default for \code{direction} is "both".
}
\item{trace}{
If \code{TRUE} (the default), information is printed during the running
diff --git a/src/Makevars.win b/src/Makevars.win
index ea686ad..34a28f2 100644
--- a/src/Makevars.win
+++ b/src/Makevars.win
@@ -1 +1 @@
-PKG_LIBS = $(BLAS_LIBS) $(FLIBS)
+PKG_LIBS = $(BLAS_LIBS) $(FLIBS)
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/r-cran-gam.git
More information about the debian-science-commits
mailing list