IRTBEMM: Family of Bayesian EMM Algorithm for Item Response Models

Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Wang (2018) <>; (4) the BEMM algorithm for 4PL model proposed by Zhang, Guo, & Zheng (2018) <>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.

Getting started

Package details

AuthorShaoyang Guo [aut, cre, cph], Chanjin Zheng [aut], Justin L Kern [aut]
MaintainerShaoyang Guo <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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IRTBEMM documentation built on Nov. 10, 2020, 3:49 p.m.