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.
How to use this package in brief
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.