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) <https:www.ncme.org/news/past-meetings/2018-recap>; (4) the BEMM algorithm for 4PL model proposed by Zhang, Guo, & Zheng (2018) <https:www.ncme.org/news/past-meetings/2018-recap>; 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.

William Kyle Hamilton <kyle.hamilton@gmail.com>, Atsushi Mizumoto
<atsushi@mizumot.com>
Interactive shiny application for running Item Response Theory
analysis.
shiny, shinyAce, beeswarm, CTT, ltm, parallel,

shiny, shinyAce, beeswarm, CTT, ltm, parallel, psych

W. Kyle Hamilton <kyle.hamilton@gmail.com>, Atsushi Mizumoto
<atsushi@mizumot.com>
Interactive shiny application for running Item Response Theory
analysis. Provides graphics for characteristic and information curves.

Parameter Estimation of Item Response Theory with Estimation of
Latent Distribution
person("Seewoo", "Li",
email = "cu@yonsei.ac.kr",
role = c("aut", "cre"))
Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm
(Bock & Aitkin, 1981 <doi:10.1007/BF02293801>).
Within parameter estimation algorithm, several methods for latent distribution estimation are available
(Li, 2022 <https://www.riss.kr/search/detail/DetailView.do?p_mat_type=be54d9b8bc7cdb09&control_no=9a95f68e2c1126c5ffe0bdc3ef48d419>).
Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly free the normality assumption on the latent distribution.