hIRT: Hierarchical Item Response Theory Models

Implementation of a class of hierarchical item response theory (IRT) models where both the mean and the variance of latent preferences (ability parameters) may depend on observed covariates. The current implementation includes both the two-parameter latent trait model for binary data and the graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM) algorithm. Asymptotic standard errors are derived from the observed information matrix.

Version: 0.3.0
Depends: R (≥ 3.4.0), stats
Imports: pryr (≥ 0.1.2), rms (≥ 5.1-1), ltm (≥ 1.1-1), Matrix (≥ 1.2-10)
Suggests: ggplot2 (≥ 2.2.1), knitr, rmarkdown
Published: 2020-03-26
DOI: 10.32614/CRAN.package.hIRT
Author: Xiang Zhou [aut, cre]
Maintainer: Xiang Zhou <xiang_zhou at fas.harvard.edu>
BugReports: http://github.com/xiangzhou09/hIRT
License: GPL (≥ 3)
URL: http://github.com/xiangzhou09/hIRT
NeedsCompilation: no
Materials: README NEWS
CRAN checks: hIRT results


Reference manual: hIRT.pdf


Package source: hIRT_0.3.0.tar.gz
Windows binaries: r-devel: hIRT_0.3.0.zip, r-release: hIRT_0.3.0.zip, r-oldrel: hIRT_0.3.0.zip
macOS binaries: r-release (arm64): hIRT_0.3.0.tgz, r-oldrel (arm64): hIRT_0.3.0.tgz, r-release (x86_64): hIRT_0.3.0.tgz, r-oldrel (x86_64): hIRT_0.3.0.tgz
Old sources: hIRT archive


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