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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 |
Reference manual: | LAWBL.pdf |
Vignettes: |
Quick Start |
Package source: | LAWBL_1.5.0.tar.gz |
Windows binaries: | r-devel: LAWBL_1.5.0.zip, r-release: LAWBL_1.5.0.zip, r-oldrel: LAWBL_1.5.0.zip |
macOS binaries: | r-release (arm64): LAWBL_1.5.0.tgz, r-oldrel (arm64): LAWBL_1.5.0.tgz, r-release (x86_64): LAWBL_1.5.0.tgz, r-oldrel (x86_64): LAWBL_1.5.0.tgz |
Old sources: | LAWBL archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.