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emIRT (version 0.0.15)

EM Algorithms for Estimating Item Response Theory Models

Description

Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) .

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Version

Install

install.packages('emIRT')

Monthly Downloads

612

Version

0.0.15

License

GPL (>= 3)

Maintainer

Kosuke Imai

Last Published

September 23rd, 2025

Functions in emIRT (0.0.15)

getStarts

Generate Starts for binIRT
binIRT

Two-parameter Binary IRT estimation via EM
makePriors

Generate Priors for binIRT
hierIRT

Hierarchichal IRT estimation via Variational Inference
AsahiTodai

Asahi-Todai Elite Survey
manifesto

German Manifesto Data
convertRC

Convert Roll Call Matrix Format
dwnom

Poole-Rosenthal DW-NOMINATE data and scores, 80-110 U.S. Senate
boot_emIRT

Parametric bootstrap of EM Standard Errirs
mq_data

Martin-Quinn Judicial Ideology Scores
dynIRT

Dynamic IRT estimation via Variational Inference
ustweet

U.S. Twitter Following Data
ordIRT

Two-parameter Ordinal IRT estimation via EM
networkIRT

Network IRT estimation via EM
poisIRT

Poisson IRT estimation via EM