Implementation of a class of hierarchical item response theory (IRT) models where both the mean and the variance of latent preferences (ability parameters) can depend on observed covariates. The current implementation includes both the two-parameter latent trait model and the graded response model. Both are fitted via the Expectation-Maximization (EM) algorithm. Asymptotic standard errors are derived from the observed information matrix.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.3.2), stats |
| Imports: | pryr (≥ 0.1.2), rms (≥ 5.1-1) |
| Suggests: | ggplot2 (≥ 2.2.1) |
| Published: | 2017-07-24 |
| 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 |
| CRAN checks: | hIRT results |
| Reference manual: | hIRT.pdf |
| Package source: | hIRT_0.1.1.tar.gz |
| Windows binaries: | r-devel: hIRT_0.1.0.zip, r-release: hIRT_0.1.0.zip, r-oldrel: not available |
| OS X El Capitan binaries: | r-release: hIRT_0.1.1.tgz |
| OS X Mavericks binaries: | r-oldrel: hIRT_0.1.1.tgz |
| Old sources: | hIRT archive |
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