LAWBL: Latent (Variable) Analysis with Bayesian Learning

An analytical framework for latent variables with different Bayesian learning methods, currently based on the partially confirmatory factor analysis (PCFA) model by Chen, Guo, Zhang, & Pan (2020) <doi:10.1037/met0000293>.

Version: 1.1.0
Depends: R (≥ 3.6.0)
Imports: stats, MASS, coda
Suggests: knitr, rmarkdown, testthat
Published: 2020-07-23
Author: Jinsong Chen [aut, cre, cph]
Maintainer: Jinsong Chen <jinsong.chen at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: LAWBL results


Reference manual: LAWBL.pdf
Vignettes: pcfa-examples
Package source: LAWBL_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: LAWBL_1.1.0.tgz, r-oldrel: LAWBL_1.1.0.tgz


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