continue to satisfy the CRAN gods

philchalmers · Nov 29, 2017 · 3147e6e · 3147e6e
1 parent 1cb5389
commit 3147e6e
Show file tree

Hide file tree

Showing 28 changed files with 44 additions and 45 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: mirt
-Version: 1.26.2
+Version: 1.26.3
 Type: Package
 Title: Multidimensional Item Response Theory
 Authors@R: c( person("Phil", family="Chalmers", email =
@@ -14,8 +14,9 @@ Authors@R: c( person("Phil", family="Chalmers", email =
     person("David", family="King", role=c('ctb')))
 Description: Analysis of dichotomous and polytomous response data using
     unidimensional and multidimensional latent trait models under the Item
-    Response Theory paradigm. Exploratory and confirmatory models can be
-    estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
+    Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
+    Exploratory and confirmatory models can be estimated with quadrature (EM) 
+    or stochastic (MHRM) methods. Confirmatory
     bi-factor and two-tier analyses are available for modeling item testlets.
     Multiple group analysis and mixed effects designs also are available for
     detecting differential item and test functioning as well as modelling

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# Changes in mirt 1.26.2
+# Changes in mirt 1.26.3
 
 - `mod2values()` and passing `pars = 'values'` now return `data.frame` objects without any
   factor variables (previously the defaults to `data.frame()` were used, which created factors
@@ -36,7 +36,8 @@
 - Fix initialization of `fscores()` when using 'MH' plausible value imputations (reported by 
   Charlie Iaconangelo)
 
-- Various other small bug fixes and performance improvements
+- Various other small bug fixes and performance improvements, fixes for Solaris compatibility,
+  and run a small number of examples during R CMD check
 
 # Changes in mirt 1.25
 

diff --git a/R/SIBTEST.R b/R/SIBTEST.R
@@ -61,6 +61,7 @@
 #'   \emph{Psychometrika, 58}, 159-194.
 #'
 #' @examples
+#'
 #' \dontrun{
 #'
 #' set.seed(1234)

diff --git a/R/areainfo.R b/R/areainfo.R
@@ -22,11 +22,11 @@
 #' @export areainfo
 #' @examples
 #'
-#' \dontrun{
 #' dat <- expand.table(LSAT7)
 #' mod <- mirt(dat, 1)
 #'
 #' areainfo(mod, c(-2,0), which.items = 1) #item 1
+#' \dontrun{
 #' areainfo(mod, c(-2,0), which.items = 1:3) #items 1 to 3
 #' areainfo(mod, c(-2,0)) # all items (total test information)
 #'

diff --git a/R/createGroup.R b/R/createGroup.R
@@ -40,8 +40,6 @@
 #' @export createGroup
 #' @examples
 #'
-#' \dontrun{
-#'
 #' # normal density example, N(mu, sigma^2)
 #' den <- function(obj, Theta) dnorm(Theta, obj@par[1], sqrt(obj@par[2]))
 #' par <- c(mu = 0, sigma2 = .5)
@@ -56,7 +54,6 @@
 #' coef(mod)
 #' coef(modcustom)
 #'
-#' }
 createGroup <- function(par, est, den, nfact, gr = NULL, hss = NULL, gen = NULL,
                        lbound = NULL, ubound = NULL, derivType = 'Richardson'){
     if(missing(par)) missingMsg('par')

diff --git a/R/expand.table.R b/R/expand.table.R
@@ -20,7 +20,6 @@
 #' @export expand.table
 #' @examples
 #'
-#' \dontrun{
 #' data(LSAT7)
 #' head(LSAT7)
 #' LSAT7full <- expand.table(LSAT7)
@@ -29,8 +28,6 @@
 #' LSAT7full <- expand.table(LSAT7, sample = TRUE)
 #' head(LSAT7full)
 #'
-#' }
-#'
 expand.table <- function(tabdata, sample = FALSE) {
     if(missing(tabdata)) missingMsg('tabdata')
     if (sum(tabdata[,ncol(tabdata)]) <= nrow(tabdata))

diff --git a/R/expected.item.R b/R/expected.item.R
@@ -19,14 +19,12 @@
 #' @seealso \code{\link{extract.item}}, \code{\link{expected.test}}
 #' @examples
 #'
-#' \dontrun{
 #' mod <- mirt(Science, 1)
 #' extr.2 <- extract.item(mod, 2)
 #' Theta <- matrix(seq(-6,6, length.out=200))
 #' expected <- expected.item(extr.2, Theta, min(Science[,1])) #min() of first item
 #' head(data.frame(expected, Theta=Theta))
 #'
-#' }
 expected.item <- function(x, Theta, min = 0){
     if(missing(x)) missingMsg('x')
     if(missing(Theta)) missingMsg('Theta')

diff --git a/R/fscores.R b/R/fscores.R
@@ -126,11 +126,11 @@
 #'
 #' @examples
 #'
-#' \dontrun{
-#'
 #' mod <- mirt(Science, 1)
 #' tabscores <- fscores(mod, full.scores = FALSE)
 #' head(tabscores)
+#'
+#' \dontrun{
 #' fullscores <- fscores(mod)
 #' fullscores_with_SE <- fscores(mod, full.scores.SE=TRUE)
 #' head(fullscores)

diff --git a/R/iteminfo.R b/R/iteminfo.R
@@ -25,7 +25,6 @@
 #' @export iteminfo
 #' @examples
 #'
-#' \dontrun{
 #' mod <- mirt(Science, 1)
 #' extr.2 <- extract.item(mod, 2)
 #' Theta <- matrix(seq(-4,4, by = .1))
@@ -34,6 +33,8 @@
 #' #do something with the info?
 #' plot(Theta, info.2, type = 'l', main = 'Item information')
 #'
+#' \dontrun{
+#'
 #' #category information curves
 #' cat.info <- iteminfo(extr.2, Theta, total.info = FALSE)
 #' plot(Theta, cat.info[,1], type = 'l', ylim = c(0, max(cat.info)),

diff --git a/R/marginal_rxx.R b/R/marginal_rxx.R
@@ -19,14 +19,15 @@
 #' @seealso \code{\link{empirical_rxx}}, \code{\link{extract.group}}, \code{\link{testinfo}}
 #' @examples
 #'
-#' \dontrun{
 #'
 #' dat <- expand.table(deAyala)
 #' mod <- mirt(dat, 1)
 #'
 #' # marginal estimate
 #' marginal_rxx(mod)
 #'
+#' \dontrun{
+#'
 #' # empirical estimate (assuming the same prior)
 #' fscores(mod, returnER = TRUE)
 #'

diff --git a/R/mdirt.R b/R/mdirt.R
@@ -73,12 +73,13 @@
 #' @export mdirt
 #' @examples
 #'
-#' \dontrun{
+
 #' #LSAT6 dataset
 #' dat <- expand.table(LSAT6)
 #'
 #' # fit with 2-3 latent classes
 #' (mod2 <- mdirt(dat, 2))
+#' \dontrun{
 #' (mod3 <- mdirt(dat, 3))
 #' summary(mod2)
 #' residuals(mod2)

diff --git a/R/mirt.R b/R/mirt.R
@@ -700,12 +700,16 @@
 #' @export mirt
 #' @examples
 #'
-#' \dontrun{
 #' #load LSAT section 7 data and compute 1 and 2 factor models
 #' data <- expand.table(LSAT7)
 #'
 #' (mod1 <- mirt(data, 1))
 #' coef(mod1)
+#' summary(mod1)
+#' plot(mod1)
+#' plot(mod1, type = 'trace')
+#'
+#' \dontrun{
 #' (mod2 <- mirt(data, 1, SE = TRUE)) #standard errors via the Oakes method
 #' (mod2 <- mirt(data, 1, SE = TRUE, SE.type = 'SEM')) #standard errors with SEM method
 #' coef(mod2)

diff --git a/R/probtrace.R b/R/probtrace.R
@@ -19,15 +19,12 @@
 #' \code{\link{extract.item}}
 #' @examples
 #'
-#' \dontrun{
 #' mod <- mirt(Science, 1)
 #' extr.2 <- extract.item(mod, 2)
 #' Theta <- matrix(seq(-4,4, by = .1))
 #' traceline <- probtrace(extr.2, Theta)
-#'
 #' head(data.frame(traceline, Theta=Theta))
 #'
-#' }
 probtrace <- function(x, Theta){
     if(missing(x)) missingMsg('x')
     if(missing(Theta)) missingMsg('Theta')

diff --git a/R/simdata.R b/R/simdata.R
@@ -84,7 +84,6 @@
 #' @export simdata
 #' @examples
 #'
-#' \dontrun{
 #' ### Parameters from Reckase (2009), p. 153
 #'
 #' set.seed(1234)
@@ -135,6 +134,8 @@
 #' #mod <- mirt(dataset1, 3, method = 'MHRM')
 #' #coef(mod)
 #'
+#' \dontrun{
+#'
 #' ### Unidimensional graded response model with 5 categories each
 #'
 #' a <- matrix(rlnorm(20,.2,.3))

diff --git a/R/testinfo.R b/R/testinfo.R
@@ -22,7 +22,6 @@
 #' @export testinfo
 #' @examples
 #'
-#' \dontrun{
 #' dat <- expand.table(deAyala)
 #' (mirt(dat, 1, '2PL', pars = 'values'))
 #' mod <- mirt(dat, 1, '2PL', constrain = list(c(1,5,9,13,17)))
@@ -31,6 +30,8 @@
 #' tinfo <- testinfo(mod, Theta)
 #' plot(Theta, tinfo, type = 'l')
 #'
+#' \dontrun{
+#'
 #' #compare information loss between two tests
 #' tinfo_smaller <- testinfo(mod, Theta, which.items = 3:5)
 #'

diff --git a/man/SIBTEST.Rd b/man/SIBTEST.Rd
diff --git a/man/areainfo.Rd b/man/areainfo.Rd
diff --git a/man/createGroup.Rd b/man/createGroup.Rd
diff --git a/man/expand.table.Rd b/man/expand.table.Rd
diff --git a/man/expected.item.Rd b/man/expected.item.Rd
diff --git a/man/fscores.Rd b/man/fscores.Rd
diff --git a/man/iteminfo.Rd b/man/iteminfo.Rd
diff --git a/man/marginal_rxx.Rd b/man/marginal_rxx.Rd
diff --git a/man/mdirt.Rd b/man/mdirt.Rd
diff --git a/man/mirt.Rd b/man/mirt.Rd