**LAWBL** is to provide a variety of models to analyze latent variables based on Bayesian learning. For more information about the package, one can see here or here.

- A design matrix Q is needed for PCFA, GPCFA, or PCIRM, but not necessary for PEFA
- Default setting can be used to minimize input (e.g., burn-in, formal iteration, maximum number of factors)
- To estimate PCFA-LI (when only a few loadings can be specified, e.g., 2 per factor), use
*m <- pcfa(dat=dat,Q=Q,LD=F)* - To estimate PCFA (with one specified loading per item), use
*m <- pcfa(dat=dat,Q=Q,LD=T)* - To estimate BREFA or FEFA (i.e., PFEA without partial information), use
*m <- pefa(dat=dat)* - To summarize basic information after estimation, use
*summary(m)* - To summarize significant loadings in pattern/Q-matrix format, use
*summary(m,what=‘qlambda’)* - To summarize factorial eigenvalues, use
*summary(m,what=‘eigen’)* - To summarize significant LD terms, use
*summary(m,what=‘offpsx’)* - To plot eigenvalues’ trace, use
*plot_lawbl(m)* - To plot eigenvalues’ density, use
*plot_lawbl(m, what=‘density’)* - To plot eigenvalues’ adjusted PSRF, use
*plot_lawbl(m, what=‘APSR’)*

You are also encouraged to visit here for an online reference of all functions.

For examples of how to use the package, see

- For PCFA with continuous data: here
- For GPCFA with categorical and mixed-type data: here
- For PCIRM with dichotomous data and intercept terms: here
- For fully and partially EFA with unknown number of factors, please refer to the
*pefa()*function.

If you would like to contribute an example to this website, please send your .Rmd file to me at jinsong.chen@live.com.