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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:1.5.0
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

Documentation:

Reference manual:LAWBL.html ,LAWBL.pdf
Vignettes:Quick Start (source)

Downloads:

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

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=LAWBLto link to this page.


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