variationalDCM

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")

Models

The following groups of models are currently supported:

• DINA model
• DINO model
• Multiple-choice-DINA model
• Saturated DCM
• Hidden Markov DCM

Acknowledgements

This package was developed as part of the project supported by JST, PRESTO Grant Number JPMJPR21C3, Japan and JSPS KAKENHI Grant Number 21H00936.

References

• 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