The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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.pdf
Vignettes: Quick Start

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 form https://CRAN.R-project.org/package=LAWBL to link to this page.

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.