mirtjml: Joint Maximum Likelihood Estimation for High-Dimensional Item
Factor Analysis
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. For more information, please refer to:
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. (2019). Structured Latent Factor Analysis for Large-scale Data:
Identifiability, Estimability, and Their Implications. Journal of the American Statistical
Association, <doi:10.1080/01621459.2019.1635485>.
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