LAWBL: Latent (Variable) Analysis with Bayesian Learning (original) (raw)
A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <doi:10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <doi:10.1007/s11336-020-09724-3>; Bayesian regularized EFA <doi:10.1080/10705511.2020.1854763>; Fully and partially EFA.
| Version: | 1.5.0 |
|---|---|
| Depends: | R (≥ 3.6.0) |
| Imports: | stats, MASS, coda |
| Suggests: | knitr, rmarkdown, testthat |
| Published: | 2022-05-16 |
| DOI: | 10.32614/CRAN.package.LAWBL |
| Author: | Jinsong Chen [aut, cre, cph] |
| Maintainer: | Jinsong Chen <jinsong.chen at live.com> |
| BugReports: | https://github.com/Jinsong-Chen/LAWBL/issues |
| License: | GPL-3 |
| URL: | https://github.com/Jinsong-Chen/LAWBL,https://jinsong-chen.github.io/LAWBL/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | Bayesian, Psychometrics |
| CRAN checks: | LAWBL results |
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