variationalDCM: Variational Bayesian Estimation for Diagnostic Classification Models

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.

Version:	2.0.1
Depends:	R (≥ 4.2.0)
Imports:	mvtnorm, stats
Suggests:	knitr
Published:	2024-03-25
DOI:	10.32614/CRAN.package.variationalDCM
Author:	Keiichiro Hijikata [aut, cre], Motonori Oka [aut], Kazuhiro Yamaguchi [aut], Kensuke Okada [aut]
Maintainer:	Keiichiro Hijikata <k.hijikata.1120 at outlook.jp>
BugReports:	https://github.com/khijikata/variationalDCM/issues
License:	GPL-3
URL:	https://github.com/khijikata/variationalDCM
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	variationalDCM results

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