External
- R Package: Latent (Variable) Analysis With Bayesian Learning (LAWBL)
Link: Github Link
Author: Prof. Jinsong Chen
Description: 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.