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hIRT
View on CRAN: Click
here
Download and install hIRT package within the R console
Install from CRAN:
install.packages("hIRT")
Install from Github:
library("remotes")
install_github("cran/hIRT") Install by package version:
library("remotes")
install_version("hIRT", "0.4.0") Attach the package and use:
library("hIRT")
Maintained by
Xiang Zhou
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-07-23
Latest Update: 2020-03-26
Description:
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.
How to cite:
Xiang Zhou (2017). hIRT: Hierarchical Item Response Theory Models. R package version 0.4.0, https://cran.r-project.org/web/packages/hIRT. Accessed 28 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for hIRT
Functions, R codes and Examples using
the hIRT R package
Some associated functions: coef_item . hgrm . hgrm2 . hltm . hltm2 . latent_scores . nes_econ2008 . print.hIRT . summary.hIRT .
Some associated R codes: coef.R . data.R . hgrm.R . hgrm2.R . hltm.R . hltm2.R . latent_scores.R . print.R . summary.R . utils.R . utils_grm.R . utils_ltm.R . Full hIRT package functions and examples
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