Title: Interaction Random Survival Forest
Description: Builds ensemble survival tree models to reveal variable interactions when the response is a time-to-events outcome. Codes contain randomization, interaction modeling, and prediction subroutines to be used in addition to the following R packages: 'survival' for Kaplan-Meier and Cox regression modeling, 'randomForestSRC' for RSF modeling, and optionally 'ggRandomForests' for Random Forest exploration/visualization. The current version contains additional R codes in folder "/inst/doc" for the analysis and generation of results shown in the corresponding article (Dazard et al. (2018) <doi:10.1515/sagmb-2017-0038>).
Imports: parallel, survival, randomForestSRC, abind

Title: Family of Bayesian EMM Algorithm for Item Response Models
Authors@R: c(person("Shaoyang", "Guo", role = c("aut", "cre", "cph"),
person("Chanjin", "Zheng", role = "aut"),
person("Justin L", "Kern", role = "aut"))
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 Bayesian Expectation-Maximization-Maximization-Maximization (BE3M) algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Wang (2018) <https:www.ncme.org/news/past-meetings/2018-recap>; (4) the BE3M 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.