Update description

DESCRIPTION

@@ -7,18 +7,12 @@ Authors@R: c(
     person("Phil", "Chalmers",
     email = "[email protected]", role = c("aut", "cre")))
 Description: Analysis of dichotomous and polytomous response data using latent
-    trait models under the Item Response Theory paradigm. Includes univariate
-    and multivariate one-, two-, three-, and four-parameter logistic models,
-    graded response models, rating scale graded response models, (generalized)
-    partial credit models, rating scale models, nominal models, multiple choice
-    models, and multivariate partially-compensatory models. Many of these
-    models can be used in an exploratory or confirmatory manner with optional
-    user defined constraints. Exploratory models can be estimated via
-    quadrature or stochastic methods, a generalized confirmatory bi-factor
-    analysis is included, and confirmatory models can be fit with a
-    Metropolis-Hastings Robbins-Monro algorithm which can include polynomial or
-    product constructed latent traits. Additionally, multiple group analysis
-    and mixed effects predictor designs may be performed for unidimensional or
+    trait models under the Item Response Theory paradigm. Exploratory models
+    can be estimated via quadrature or stochastic methods, a generalized
+    confirmatory bi-factor analysis is included, and confirmatory models can be
+    fit with a Metropolis-Hastings Robbins-Monro algorithm which can include
+    polynomial or product constructed latent traits. Multiple group analysis
+    and mixed effects designs may be performed for unidimensional or
     multidimensional item response models for detecting differential item
     functioning and modelling item and person covariates.
 Depends:

R/mirt-package.R

@@ -1,18 +1,14 @@
 #' Full information maximum likelihood estimation of multidimensional IRT models
 #' 
 #' Analysis of dichotomous and polytomous response data using latent
-#' trait models under the Item Response Theory paradigm. Includes univariate
-#' and multivariate one-, two-, three-, and four-parameter logistic models,
-#' graded response models, generalized partial credit models, nominal models,
-#' multiple choice models, and multivariate partially-compensatory models.
-#' These can be used in an exploratory or confirmatory manner with optional
-#' user defined linear constraints. Exploratory models can be estimated via
-#' quadrature or stochastic methods, a generalized confirmatory bi-factor
+#' trait models under the Item Response Theory paradigm. Exploratory models can be 
+#' estimated via quadrature or stochastic methods, a generalized confirmatory bi-factor
 #' analysis is included, and confirmatory models can be fit with a
 #' Metropolis-Hastings Robbins-Monro algorithm which can include polynomial or
-#' product constructed latent traits. Additionally, multiple group analysis may
-#' be performed for unidimensional or multidimensional item response models for
-#' detecting differential item functioning.
+#' product constructed latent traits. Multiple group analysis
+#' and mixed effects designs may be performed for unidimensional or
+#' multidimensional item response models for detecting differential item
+#' functioning and modelling item and person covariates.
 #' 
 #' Users interested in the most recent version of this package can visit 
 #' \code{https://github.com/philchalmers/mirt} and follow the instructions 

man/mirt-package.Rd

@@ -9,21 +9,16 @@
 \details{
   Analysis of dichotomous and polytomous response data
   using latent trait models under the Item Response Theory
-  paradigm. Includes univariate and multivariate one-,
-  two-, three-, and four-parameter logistic models, graded
-  response models, generalized partial credit models,
-  nominal models, multiple choice models, and multivariate
-  partially-compensatory models. These can be used in an
-  exploratory or confirmatory manner with optional user
-  defined linear constraints. Exploratory models can be
-  estimated via quadrature or stochastic methods, a
-  generalized confirmatory bi-factor analysis is included,
-  and confirmatory models can be fit with a
-  Metropolis-Hastings Robbins-Monro algorithm which can
-  include polynomial or product constructed latent traits.
-  Additionally, multiple group analysis may be performed
-  for unidimensional or multidimensional item response
-  models for detecting differential item functioning.
+  paradigm. Exploratory models can be estimated via
+  quadrature or stochastic methods, a generalized
+  confirmatory bi-factor analysis is included, and
+  confirmatory models can be fit with a Metropolis-Hastings
+  Robbins-Monro algorithm which can include polynomial or
+  product constructed latent traits. Multiple group
+  analysis and mixed effects designs may be performed for
+  unidimensional or multidimensional item response models
+  for detecting differential item functioning and modelling
+  item and person covariates.
 
   Users interested in the most recent version of this
   package can visit