Description: 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.

Author: William Kyle Hamilton <kyle.hamilton@gmail.com>, Atsushi Mizumoto
Description: Interactive shiny application for running Item Response Theory
Imports: shiny, shinyAce, beeswarm, CTT, ltm, parallel,

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

Author: W. Kyle Hamilton <kyle.hamilton@gmail.com>, Atsushi Mizumoto
Maintainer: W. Kyle Hamilton <kyle.hamilton@gmail.com>
analysis. Provides graphics for characteristic and information curves.

Title: Parameter Estimation of Item Response Theory with Estimation of
Description: Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm
Within parameter estimation algorithm, several methods for latent distribution estimation are available
Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly free the normality assumption on the latent distribution.