Variational Bayesian Estimation for Diagnostic Classification Models R package variationalDCM version 2.0.1 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. Software Depends: R (>= 4.2.0) Imports: mvtnorm, stats Suggests: knitr Keiichiro Hijikata <k.hijikata.1120@outlook.jp> Comprehensive R Archive Network (CRAN) Keiichiro Hijikata [aut, cre], Motonori Oka [aut] (ORCID: <https://orcid.org/0000-0002-9867-8922>), Kazuhiro Yamaguchi [aut] (ORCID: <https://orcid.org/0000-0001-8011-8575>), Kensuke Okada [aut] (ORCID: <https://orcid.org/0000-0003-1663-5812>) GPL-3 2024-03-25 application/tgz https://CRAN.R-project.org/package=variationalDCM doi:10.32614/CRAN.package.variationalDCM