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-4.5)LAWBL_1.5.2.zip(r-4.4)LAWBL_1.5.2.zip(r-4.3)
LAWBL_1.5.2.tgz(r-4.4-any)LAWBL_1.5.2.tgz(r-4.3-any)
LAWBL_1.5.2.tar.gz(r-4.5-noble)LAWBL_1.5.2.tar.gz(r-4.4-noble)
LAWBL_1.5.2.tgz(r-4.4-emscripten)LAWBL_1.5.2.tgz(r-4.3-emscripten)
LAWBL.pdf |LAWBL.html✨
LAWBL/json (API)
NEWS
| # 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
- 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 2 years agofrom:593d49b936. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | OK | Nov 13 2024 |
| R-4.5-linux | OK | Nov 13 2024 |
| R-4.4-win | OK | Nov 13 2024 |
| R-4.4-mac | OK | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
Exports:pcfapcirmpefaplot_lawblsim_lvm
