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.


hIRT 0.1.3

  • removed default x and z values in hgrm() and hltm()

Reference manual

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0.3.0 by Xiang Zhou, a year ago


Report a bug at http://github.com/xiangzhou09/hIRT

Browse source code at https://github.com/cran/hIRT

Authors: Xiang Zhou [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports pryr, rms, ltm, Matrix

Depends on stats

Suggests ggplot2, knitr, rmarkdown

See at CRAN