LAWBL
1.5.0Latent (Variable) Analysis with Bayesian Learning
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 17%
- Documented parameters
- 100%
- Return-value docs
- 80%
- References docs
- 29%
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Checks run against github.com/jinsong-chen/lawbl on 2026-07-19.
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Package metadata
- First published
- 2020-07-23
- Total releases
- 4 / 6 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.6.0
- Bundled data
- 436 KB / 7 files
- Download size
- 482 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet