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:Jinsong Chen [aut, cre, cph]

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)
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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'))

Peer review:

Bug tracker:https://github.com/jinsong-chen/lawbl/issues

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

On CRAN:

4.48 score 6 stars 5 scripts 277 downloads 5 exports 3 dependencies

Last updated 2 years agofrom:593d49b936. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 14 2024
R-4.5-winOKOct 14 2024
R-4.5-linuxOKOct 14 2024
R-4.4-winOKOct 14 2024
R-4.4-macOKOct 14 2024
R-4.3-winOKOct 14 2024
R-4.3-macOKOct 14 2024

Exports:pcfapcirmpefaplot_lawblsim_lvm

Dependencies:codalatticeMASS

Quick Start

Rendered fromLAWBL.Rmdusingknitr::rmarkdownon Oct 14 2024.

Last update: 2022-05-15
Started: 2021-03-19