Methods and Tools for Bayesian Variable Selection and Model Averaging in Univariate Linear Regression

TL;DR

This paper reviews Bayesian methods for variable selection in univariate linear regression, focusing on prior elicitation, posterior summaries, and computational strategies, and compares R packages for practical implementation.

Contribution

It provides a comprehensive comparison of R packages for Bayesian variable selection, highlighting differences in flexibility and efficiency.

Findings

All packages produce similar results.

Differences exist in flexibility and computational efficiency.

Recommendations for applied users are provided.

Abstract

In this paper we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available {\tt R}-packages for its practical implementation summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.