variationalDCM

Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) doi:10.1007/s11336-020-09739-w, Yamaguchi and Okada (2020b) doi:10.3102/1076998620911934, Yamaguchi (2020) doi:10.1007/s41237-020-00104-w, Oka and Okada (2023) doi:10.1007/s11336-022-09884-4, and Yamaguchi and Martinez (2023) doi:10.1111/bmsp.12308. To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.

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