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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.
Getting started
Browse package contents
Package details |
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| Author | Jinsong Chen [aut, cre, cph] |
| Maintainer | Jinsong Chen <jinsong.chen@live.com> |
| License | GPL-3 |
| Version | 1.5.0 |
| URL | https://github.com/Jinsong-Chen/LAWBL https://jinsong-chen.github.io/LAWBL/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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