FBMS让复杂回归模型的贝叶斯选择与平均更高效，支持非线性特征自动生成。
FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
- 采用改进的马尔可夫链蒙特卡洛算法，提升多峰后验分布的采样效率。
- 可自动生成非线性特征并评估其后验概率，支持非线性模型建模。
- 适用于高维回归、混合效应模型等复杂场景，适合统计建模研究者。
FBMS R 包通过多种蒙特卡洛模型探索方法，实现复杂回归设置下的贝叶斯模型选择与平均。核心是高效的模式跳跃马尔可夫链蒙特卡洛（MJMCMC）算法，用于改善贝叶斯广义线性模型中多峰后验空间的混合性能。此外，该包引入遗传改良版的 MJMCMC（GMJMCMC）算法，通过非线性特征生成，实现贝叶斯广义非线性模型（BGNLMs）的估计。在该框架下，算法维护并更新变换特征种群，计算其后验概率，并评估由它们构建的模型后验。我们展示了 FBMS 在高斯回归中的推断与预测建模应用，涵盖 BGNLM 的不同实例。并通过广泛的应用案例，说明该方法可扩展至更多复杂建模场景，包括其他响应分布和混合效应模型。
原文摘要 · Abstract (English)
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.
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