LAWBL represents a partially exploratory-confirmatory approach to model latent variables based on Bayesian learning. Built on the power of statistical learning, it can address psychometric challenges such as parameter specification, local dependence, and factor extraction. Built on the scalability and flexibility of Bayesian inference and resampling techniques, it can accommodate modeling frameworks such as factor analysis, item response theory, cognitive diagnosis modeling and causal or explanatory modeling. The package can also handle different response formats or a mix of them, with or without missingness.
- Partially CFA (PCFA) for continuous data: regularization of loading specification and local dependence; PCFA with local independence (PCFA-LI); CFA with local dependence (CFA-LD)
- Generalized PCFA (GPCFA) for continuous, categorical, or mixed-type data, with or without missingness; GPCFA with local independence (GPCFA-LI); Generalized CFA with local dependence (GCFA-LD)
- Partially confirmatory item response model (PCIRM) for continuous and dichotomous data with intercept terms; PCIRM-LI; CIRM-LD
- Bayesian regularized EFA (BREFA): factor extraction and parameter estimation in one step; Fully and partially EFA: unknown number of factors without or with partial knowledge
- Estimation using different Bayesian learning methods and MCMC algorithms
- Simulating data based on all aforementioned models
- Plotting trace, density or Gelman-Rubin diagnostics based on eigenvalue
- Summary of all parameters with both point and interval estimates
Please refer to the online tutorials for more details.
1) Install the stable version from CRAN with:
2) Install the
devtools package (if necessary), and install the development version from the Github.