LAWBL: Latent (Variable) Analysis With Bayesian Learning

Project Status: Active ? The project has reached a stable, usable state and is being actively developed. CRAN_Status_Badge

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


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.


Please refer to the online tutorials for more details.


  1. Install the stable version from CRAN with:
  1. Install the devtools package (if necessary), and install the development version from the Github.
# install.packages("devtools")


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.