---
title: "Quick Start"
data: "'r Sys.Date()'"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Quick Start}
  %\VignetteEncoding{UTF-8}{inputenc}
  <!-- \usepackage[utf8]{inputenc} -->
  %\VignetteEngine{knitr::rmarkdown}
---

**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](https://jinsong-chen.github.io/LAWBL/index.html) or [here](https://jinsong-chen.github.io/LAWBL/reference/LAWBL-package.html).

## 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](https://jinsong-chen.github.io/LAWBL/reference/index.html) for an online reference of all functions.


For examples of how to use the package, see

* For PCFA with continuous data: [here](https://jinsong-chen.github.io/LAWBL/articles/Examples/pcfa-examples.html) 
* For GPCFA with categorical and mixed-type data: [here](https://jinsong-chen.github.io/LAWBL/articles/Examples/gpcfa-examples.html) 
* For PCIRM with dichotomous data and intercept terms: [here](https://jinsong-chen.github.io/LAWBL/articles/Examples/pcirm-examples.html) 
* 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.
