LAWBL: Latent (Variable) Analysis with Bayesian Learning

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: 1.4.0
Depends: R (≥ 3.6.0)
Imports: stats, MASS, coda
Suggests: knitr, rmarkdown, testthat
Published: 2021-04-01
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

Downloads:

Reference manual: LAWBL.pdf
Vignettes: Quick Start
Package source: LAWBL_1.4.0.tar.gz
Windows binaries: r-devel: LAWBL_1.4.0.zip, r-release: LAWBL_1.4.0.zip, r-oldrel: LAWBL_1.4.0.zip
macOS binaries: r-release (arm64): LAWBL_1.4.0.tgz, r-release (x86_64): LAWBL_1.4.0.tgz, r-oldrel: LAWBL_1.4.0.tgz
Old sources: LAWBL archive

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