Model BIC Posterior Probability.
modelbpp: Model BIC Posterior Probability 
(Version 0.2.0 updated on 2026-03-01, release history)
This package is for assessing model uncertainty in structural equation modeling (SEM) by the BIC posterior probabilities of the fitted model and its neighboring models, based on the method presented in Wu, Cheung, and Leung (2020). The package name, modelbpp, stands for modelbayesian posterior probability. An introduction to the package can be found in the following article:
- Pesigan, I. J. A., Cheung, S. F., Wu, H., Chang, F., & Leung, S. O. (2026). How plausible is my model? Assessing model plausibility of structural equation models using Bayesian posterior probabilities (BPP). Behavior Research Methods, 58(3), Article 73. https://doi.org/10.3758/s13428-025-02921-x
Homepage
For more information on this package, please visit its GitHub page:
https://sfcheung.github.io/modelbpp/
A quick introduction on how to use this package can be found in the Get-Started article (vignette("modelbpp")).
Installation
The stable CRAN version can be installed by install.packages():
install.packages("modelbpp")
The latest developmental-but-stable version of this package can be installed by remotes::install_github:
remotes::install_github("sfcheung/modelbpp")
Issues
If you have any suggestions or found any bugs, please feel free to open a GitHub issue. Thanks.
https://github.com/sfcheung/modelbpp/issues
Reference(s)
Wu, H., Cheung, S. F., & Leung, S. O. (2020). Simple use of BIC to assess model selection uncertainty: An illustration using mediation and moderation models. Multivariate Behavioral Research, 55(1), 1--16. https://doi.org/10.1080/00273171.2019.1574546