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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.1 |
rolling linux/jammy R-4.5 | variationalDCM_2.0.1.tar.gz |
136.5 KiB |
2.0.1 |
rolling linux/noble R-4.5 | variationalDCM_2.0.1.tar.gz |
136.3 KiB |
2.0.1 |
rolling source/ R- | variationalDCM_2.0.1.tar.gz |
29.9 KiB |
2.0.1 |
latest linux/jammy R-4.5 | variationalDCM_2.0.1.tar.gz |
136.5 KiB |
2.0.1 |
latest linux/noble R-4.5 | variationalDCM_2.0.1.tar.gz |
136.3 KiB |
2.0.1 |
latest source/ R- | variationalDCM_2.0.1.tar.gz |
29.9 KiB |
2.0.1 |
2026-04-26 source/ R- | variationalDCM_2.0.1.tar.gz |
29.9 KiB |
2.0.1 |
2026-04-23 source/ R- | variationalDCM_2.0.1.tar.gz |
29.9 KiB |
2.0.1 |
2026-04-09 windows/windows R-4.5 | variationalDCM_2.0.1.zip |
141.9 KiB |
2.0.1 |
2025-04-20 source/ R- | variationalDCM_2.0.1.tar.gz |
29.9 KiB |