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variationalDCM is an R package that performs recently-developed variational Bayesian inference for diagnostic classification models (DCMs), which are a family of statistical models for collecting, analyzing, and reporting diagnostic information in Education and Psychology.
You can install this package from CRAN at https://cran.r-project.org/package=variationalDCM. Alternatively, a development version can be installed using the devtools package:
if(!require(devtools)){
install.packages("devtools")
}
devtools::install_github("khijikata/variationalDCM")The following groups of models are currently supported: - DINA model - DINO model - Multiple-choice-DINA model - Saturated DCM - Hidden Markov DCM
This package was developed as part of the project supported by JST, PRESTO Grant Number JPMJPR21C3, Japan and JSPS KAKENHI Grant Number 21H00936.
Oka, M., & Okada, K. (2023). Scalable Bayesian Approach for the Dina Q-Matrix Estimation Combining Stochastic Optimization and Variational Inference. Psychometrika. https://doi.org/10.1007/s11336-022-09884-4
Yamaguchi, K., & Okada, K. (2020). Variational Bayes Inference for the DINA Model. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998620911934
Yamaguchi, K., & Okada, K. (2020). Variational Bayes Inference Algorithm for the Saturated Diagnostic Classification Model. Psychometrika. https://doi.org/10.1007/s11336-020-09739-w
Yamaguchi, K. (2020). Variational Bayesian inference for the multiple-choice DINA model. Behaviormetrika. https://doi.org/10.1007/s41237-020-00104-w
Yamaguchi, K., & Martinez, A. J. (2024). Variational Bayes inference for hidden Markov diagnostic classification models. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12308
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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