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hIRT  

Hierarchical Item Response Theory Models
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", "0.3.0")



Attach the package and use:
library("hIRT")
Maintained by
Xiang Zhou
[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 0.3.0, https://cran.r-project.org/web/packages/hIRT. Accessed 06 Mar. 2026.
Previous versions and publish date:
0.1.0 (2017-07-23 22:06), 0.1.1 (2017-07-24 23:27), 0.1.2 (2017-08-01 21:00), 0.1.3 (2018-09-17 01:10), 0.2.0 (2019-06-13 19:50), 0.3.0 (2020-03-26 18:10)
Other packages that cited hIRT R package
View hIRT citation profile
Other R packages that hIRT depends, 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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