FBMS: An R package for flexible Bayesian model selection and averaging with nonlinear models

The FBMS R package provides a unified interface for Bayesian model selection and model averaging across a broad class of regression models, including Gaussian regression, generalized linear models, and models with nonlinear functional relationships, with possible extensions to mixed-effects and survival models. Users specify candidate predictors together with transformation and interaction rules, and FBMS automatically explores the corresponding model spaces, estimates marginal likelihoods, and returns posterior model and inclusion probabilities, best and median probability models, posterior modes of parameters, and predictions with associated uncertainty. The core machinery combines mode-jumping Markov chain Monte Carlo with a genetically modified extension that iteratively generates and evaluates nonlinear features in the framework of Bayesian generalized nonlinear regression, while supporting flexible prior specification (including g-priors, Jeffreys priors, and empirical Bayes constructions), subsampling for large datasets, parallel computation, and the option to supply custom likelihoods for specialized applications. As special cases, the framework covers models based on fractional polynomials, logic regression, neural networks, symbolic regression, and classical linear regression.