How to use this package in brief
Package: LAWBL 1.5.2
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.
Authors:
LAWBL_1.5.2.tar.gz
LAWBL_1.5.2.zip(r-4.7)LAWBL_1.5.2.zip(r-4.6)LAWBL_1.5.2.zip(r-4.5)
LAWBL_1.5.2.tgz(r-4.6-any)LAWBL_1.5.2.tgz(r-4.5-any)
LAWBL_1.5.2.tar.gz(r-4.7-any)LAWBL_1.5.2.tar.gz(r-4.6-any)
LAWBL_1.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LAWBL/json (API)
| # Install 'LAWBL' in R: |
| install.packages('LAWBL', repos = c('https://jinsong-chen.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jinsong-chen/lawbl/issues
Pkgdown/docs site:https://jinsong-chen.github.io
Datasets:
- nlsy27 - National Longitudinal Survey of Youth 1997
- sim18ccfa40 - Simulated CCFA data with LI and missingness
- sim18ccfa41 - Simulated CCFA data with LD and missingness
- sim18cfa0 - Simulated CFA data with LI
- sim18cfa1 - Simulated CFA data with LD
- sim18mcfa41 - Simulated MCFA data with LD and Missingness
- sim24ccfa21 - Simulated CCFA data (dichotomous) with LD and a minor factor/trait
Last updated from:593d49b936. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 169 | ||
| source / vignettes | OK | 229 | ||
| linux-release-x86_64 | OK | 165 | ||
| macos-release-arm64 | OK | 278 | ||
| macos-oldrel-arm64 | OK | 229 | ||
| windows-devel | OK | 116 | ||
| windows-release | OK | 115 | ||
| windows-oldrel | OK | 113 | ||
| wasm-release | OK | 102 |
Exports:pcfapcirmpefaplot_lawblsim_lvm
