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Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Version: | 0.1-7 |
Depends: | ltm |
Imports: | Rcpp (≥ 0.12.4), TeachingDemos, cubature, caret, statmod, mvtnorm, mirt, methods |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2024-01-23 |
DOI: | 10.32614/CRAN.package.GPCMlasso |
Author: | Gunther Schauberger |
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 |
Reference manual: | GPCMlasso.pdf |
Package source: | GPCMlasso_0.1-7.tar.gz |
Windows binaries: | r-devel: GPCMlasso_0.1-7.zip, r-release: GPCMlasso_0.1-7.zip, r-oldrel: GPCMlasso_0.1-7.zip |
macOS binaries: | r-release (arm64): GPCMlasso_0.1-7.tgz, r-oldrel (arm64): GPCMlasso_0.1-7.tgz, r-release (x86_64): GPCMlasso_0.1-7.tgz, r-oldrel (x86_64): GPCMlasso_0.1-7.tgz |
Old sources: | GPCMlasso archive |
Reverse enhances: | mnlfa |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.