Hierarchical Item Response Theory Models
Overview
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. See Zhou (2019) doi:10.1017/pan.2018.63 for details.
- Removed dependency on the archived package pryr; function composition is now done in base R.
- Added
hgrmDIF()for differential item functioning in the graded response model.
From NEWS.md in the release
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- OK6 OK2026-09-29
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- Help pages
- 10
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- 88%7 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 20%
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- unarchivedReturned to CRAN2026-09-28
- 0.4.0Latest
- RR 4.6.0 released · 2026-04-24
- archivedRemoved from CRAN2026-01-30requires archived package 'pryr'
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 0.3.0
- 0.2.0
- RR 3.6.0 released · 2019-04-26
- 0.1.32018-09-16 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 0.1.22017-08-01 · diff ↗
Package metadata
- First published
- 2017-07-23
- Total releases
- 7 / 9 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.4.0
- Bundled data
- 7.4 KB / 1 file
- Download size
- 108 KB
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