GPCMlasso: Regularized Explanatory Generalized Partial Credit Models

Fits explanatory generalized partial credit models and related ordinal item response models with global and item-specific covariate effects. Penalized marginal maximum likelihood estimation is used for variable selection, detection of differential item functioning, and clustering of item-specific covariate effects by fusion penalties. The package extends the regularization approach for differential item functioning in generalized partial credit models proposed by Schauberger and Mair (2020) <doi:10.3758/s13428-019-01224-2>.

Version:	0.2-0
Depends:	ltm
Imports:	Rcpp (≥ 0.12.4), TeachingDemos, cubature, caret, statmod, mvtnorm, mirt, methods
LinkingTo:	Rcpp, RcppArmadillo
Published:	2026-09-08
DOI:	10.32614/CRAN.package.GPCMlasso
Author:	Gunther Schauberger [aut, cre]
Maintainer:	Gunther Schauberger <gunther.schauberger at tum.de>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
In views:	Psychometrics
CRAN checks:	GPCMlasso results

Documentation:

Reference manual:

GPCMlasso.html , GPCMlasso.pdf

Downloads:

Package source:	GPCMlasso_0.2-0.tar.gz
Windows binaries:	r-devel: GPCMlasso_0.1-9.zip, r-release: GPCMlasso_0.2-0.zip, r-oldrel: GPCMlasso_0.1-9.zip
macOS binaries:	r-release (arm64): GPCMlasso_0.1-9.tgz, r-oldrel (arm64): GPCMlasso_0.1-9.tgz, r-release (x86_64): GPCMlasso_0.2-0.tgz, r-oldrel (x86_64): GPCMlasso_0.2-0.tgz
Old sources:	GPCMlasso archive

Reverse dependencies:

Reverse enhances:

mnlfa

Linking:

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