A collection of Item Response Theory (IRT) and Computerized 
     Adaptive Testing (CAT) functions that are used in psychometrics.

Analysis of Dichotomous and polytomous data using unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user friendly Graphical User Interface. Suitable for  beginners who are learning Item Response Theory.

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, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; 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.

Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.

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.
    Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.