IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters

IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters

A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G,...

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Veröffentlicht in:Applied psychological measurement 2020-10, Vol.44 (7-8), p.566-567
Hauptverfasser: Guo, Shaoyang, Zheng, Chanjin, Kern, Justin L.
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Computer Program Exchange
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container_title Applied psychological measurement
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Zheng, Chanjin
Kern, Justin L.
description A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.
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title IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters
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