Package: hIRT 0.4.0

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.

Authors:Xiang Zhou [aut, cre]

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Bug tracker:https://github.com/xiangzhou09/hirt/issues

On CRAN:

9 exports 12 stars 1.59 score 89 dependencies 11 scripts 191 downloads

Last updated 3 years agofrom:b33d90f132. Checks:OK: 7. Indexed: yes.

Exports:coef_itemcoef_meancoef_varhgrmhgrm2hgrmDIFhltmhltm2latent_scores

Dependencies:admiscbackportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayondata.tabledigestevaluateexpmfansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclelobstrltmmagrittrMASSMatrixMatrixModelsmemoisemgcvmimemsmmultcompmunsellmvtnormnlmennetpillarpkgconfigpolsplinepolycorprettyunitspryrquantregR6rappdirsRColorBrewerRcpprlangrmarkdownrmsrpartrstudioapisandwichsassscalesSparseMstringistringrsurvivalTH.datatibbletinytexutf8vctrsviridisviridisLitewithrxfunyamlzoo