mirtjml is a R package provides constrained joint maximum likelihood estimation algorithms for item factor analysis (IFA) based on multidimensional item response theory models. So far, we provide functions for exploratory and confirmatory IFA based on the multidimensional two parameter logistic (M2PL) model for binary response data. Comparing with traditional estimation methods for IFA, the methods implemented in this package scale better to data with large numbers of respondents, items, and latent factors. The computation is facilitated by multiprocessing 'OpenMP’ API.

Major Features

  • Use a constraint maximum likelihood estimation with alternate minimization algorithm to solve generalized multidimensional item factor analysis.

  • Adaptively decides step size of optimization.

  • Written in R with core functions implemented in C++.

  • OpenMP C++ parallel API is employed for speeding up the algorithm using modern multi-core CPUs.


mirtjml package has been published on CRAN. Install it simply by running the following command in R console.

> install.packages(“mirtjml”)

Source code can be found on mirtjml's GitHub page.


  1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23. <doi:10.1007/s11336-018-9646-5>.

  2. Chen, Y., Li, X., & Zhang, S. (2017). Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications. arXiv preprint <arXiv:1712.08966>