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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.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:

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