Abstract
A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.
References
|
Guo, S., Zheng, C. (2019). The Bayesian expectation-maximization-maximization for the 3PLM. Frontiers in Psychology, 10, Article 1175. https://doi.org/10.3389/fpsyg.2019.01175 Google Scholar | |
|
Guo, S., Zheng, C., Kern, J. L. (2020). IRTBEMM: Family of Bayesian EMM algorithm for item response models (Version 1.0.2) [R package]. https://cran.r-project.org/package=IRTBEMM Google Scholar | |
|
Zhang, C., Guo, S., Zheng, C. (2018, April). Bayesian expectation-maximization-maximization algorithm for the 4PLM. Paper presented at the 80th NCME annual meeting. Google Scholar | |
|
Zheng, C., Meng, X., Guo, S., Liu, Z. (2018). Expectation-maximization-maximization: A feasible MLE algorithm for the three-parameter logistic model based on a mixture modeling reformulation. Frontiers in Psychology, 8, Article 2302. https://doi.org/10.3389/fpsyg.2017.02302 Google Scholar |

