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LAWBL  

Latent (Variable) Analysis with Bayesian Learning
View on CRAN: Click here


Download and install LAWBL package within the R console
Install from CRAN:
install.packages("LAWBL")

Install from Github:
library("remotes")
install_github("cran/LAWBL")

Install by package version:
library("remotes")
install_version("LAWBL", "1.5.0")



Attach the package and use:
library("LAWBL")
Maintained by
Jinsong Chen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-07-23
Latest Update: 2022-05-16
Description:
A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) ; generalized PCFA; partially confirmatory IRM (Chen, 2020) ; Bayesian regularized EFA ; Fully and partially EFA.
How to cite:
Jinsong Chen (2020). LAWBL: Latent (Variable) Analysis with Bayesian Learning. R package version 1.5.0, https://cran.r-project.org/web/packages/LAWBL. Accessed 09 Mar. 2025.
Previous versions and publish date:
1.1.0 (2020-07-23 18:22), 1.3.0 (2020-11-03 21:10), 1.4.0 (2021-04-01 23:30)
Other packages that cited LAWBL R package
View LAWBL citation profile
Other R packages that LAWBL depends, imports, suggests or enhances
Complete documentation for LAWBL
Functions, R codes and Examples using the LAWBL R package
Some associated functions: LAWBL-package . nlsy27 . pcfa . pcirm . pefa . plot_lawbl . sim18ccfa40 . sim18ccfa41 . sim18cfa0 . sim18cfa1 . sim18mcfa41 . sim24ccfa21 . sim_lvm . summary.lawbl . 
Some associated R codes: Gibbs_BLR_SSP.R . Gibbs_LA_IYC.R . Gibbs_LA_IYE.R . Gibbs_MU.R . Gibbs_PSX.R . GwMH_LA_MYC.R . GwMH_LA_MYE.R . Init.R . LAWBL-package.R . Omega_Phi.R . datasets.R . pcfa.R . pcirm.R . pefa.R . plot_lawbl.R . sim_lvm.R . summary.lawbl.R . utils.R . zzz.R .  Full LAWBL package functions and examples
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