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LAWBL: Latent (Variable) Analysis With Bayesian Learning

How to cite the package

Chen, J. (2022). LAWBL: Latent (variable) analysis with Bayesian learning (R package version 1.5.0). Retrieved from https://CRAN.R-project.org/package=LAWBL

Introduction

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.

Features

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.

Installation

Install the stable version from CRAN with:

install.packages("LAWBL")

Install the devtools package (if necessary), and install the development version from the Github.

# install.packages("devtools")
devtools::install_github("Jinsong-Chen/LAWBL")

References

Chen, J. (2020). A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso. Psychometrika. 85(3), 738-774. DOI: 10.1007/s11336-020-09724-3.

Chen, J., Guo, Z., Zhang, L., & Pan, J. (2021). A partially confirmatory approach to scale development with the Bayesian Lasso. Psychological Methods. 26(2), 210–235. DOI: 10.1037/met0000293.

Chen, J. (2021). A generalized partially confirmatory factor analysis framework with mixed Bayesian Lasso methods. Multivariate Behavioral Research. DOI: 10.1080/00273171.2021.1925520.

Chen, J. (2021). A Bayesian regularized approach to exploratory factor analysis in one step. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/10705511.2020.1854763.

Chen, J. (2022). Partially confirmatory approach to factor analysis with Bayesian learning: A LAWBL tutorial. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/00273171.2021.1925520.

Chen, J. (In Press). Fully and partially exploratory factor analysis with bi-level Bayesian regularization. Behavior Research Methods.