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emIRT (version 0.0.14)

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

265

Version

0.0.14

License

GPL (>= 3)

Maintainer

Last Published

July 6th, 2024

Functions in emIRT (0.0.14)

binIRT

Two-parameter Binary IRT estimation via EM
getStarts

Generate Starts for binIRT
manifesto

German Manifesto Data
dwnom

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

Asahi-Todai Elite Survey
dynIRT

Dynamic IRT estimation via Variational Inference
convertRC

Convert Roll Call Matrix Format
boot_emIRT

Parametric bootstrap of EM Standard Errirs
makePriors

Generate Priors for binIRT
hierIRT

Hierarchichal IRT estimation via Variational Inference
ustweet

U.S. Twitter Following Data
mq_data

Martin-Quinn Judicial Ideology Scores
ordIRT

Two-parameter Ordinal IRT estimation via EM
networkIRT

Network IRT estimation via EM
poisIRT

Poisson IRT estimation via EM