FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
This work provides a tool for statisticians and data analysts to perform flexible Bayesian model selection and averaging, though it is incremental as it builds on existing Monte Carlo methods with specific algorithmic enhancements.
The authors tackled the problem of Bayesian model selection and averaging in complex regression settings by developing the FBMS R package, which implements efficient Monte Carlo methods including MJMCMC and GMJMCMC algorithms, resulting in improved mixing for multi-modal posteriors and enabling nonlinear feature generation for Bayesian generalized nonlinear models.
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its core, the package implements an efficient Mode Jumping Markov Chain Monte Carlo (MJMCMC) algorithm, designed to improve mixing in multi-modal posterior landscapes within Bayesian generalized linear models. In addition, it provides a genetically modified MJMCMC (GMJMCMC) algorithm that introduces nonlinear feature generation, thereby enabling the estimation of Bayesian generalized nonlinear models (BGNLMs). Within this framework, the algorithm maintains and updates populations of transformed features, computes their posterior probabilities, and evaluates the posteriors of models constructed from them. We demonstrate the effective use of FBMS for both inferential and predictive modeling in Gaussian regression, focusing on different instances of the BGNLM class of models. Furthermore, through a broad set of applications, we illustrate how the methodology can be extended to increasingly complex modeling scenarios, extending to other response distributions and mixed effect models.