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 ORCID iD [aut], Kazuhiro Yamaguchi ORCID iD [aut], Kensuke Okada ORCID iD [aut]
Maintainer: Keiichiro Hijikata <k.hijikata.1120 at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: variationalDCM results


Reference manual: variationalDCM.pdf
Vignettes: variationalDCM vignette


Package source: variationalDCM_2.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): variationalDCM_2.0.1.tgz, r-oldrel (arm64): variationalDCM_2.0.1.tgz, r-release (x86_64): variationalDCM_2.0.1.tgz, r-oldrel (x86_64): variationalDCM_2.0.1.tgz
Old sources: variationalDCM archive


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