LAWBL: Latent (Variable) Analysis with Bayesian Learning

An analytical framework for latent variables with different Bayesian learning methods, including the partially confirmatory factor analysis (Chen, Guo, Zhang, & Pan, 2020) <doi:10.1037/met0000293>, its generalized version, and the partially confirmatory item response model (Chen, 2020) <doi:10.1007/s11336-020-09724-3>.

Version: 1.3.0
Depends: R (≥ 3.6.0)
Imports: stats, MASS, coda
Suggests: knitr, rmarkdown, testthat
Published: 2020-11-03
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: Psychometrics
CRAN checks: LAWBL results

Downloads:

Reference manual: LAWBL.pdf
Vignettes: gpcfa-examples
pcfa-examples
pcirm-examples
Package source: LAWBL_1.3.0.tar.gz
Windows binaries: r-devel: LAWBL_1.3.0.zip, r-release: LAWBL_1.3.0.zip, r-oldrel: LAWBL_1.3.0.zip
macOS binaries: r-release: LAWBL_1.3.0.tgz, r-oldrel: LAWBL_1.3.0.tgz
Old sources: LAWBL archive

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