varbvs: Fast Variable Selection for Large-scale Regression

Peter Carbonetto; Xiang Zhou; Matthew Stephens

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arXiv · 1709.06597

varbvs: Fast Variable Selection for Large-scale Regression

Peter Carbonetto·Xiang Zhou·Matthew Stephens

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Abstract

We introduce varbvs, a suite of functions written in R and MATLAB for regression analysis of large-scale data sets using Bayesian variable selection methods. We have developed numerical optimization algorithms based on variational approximation methods that make it feasible to apply Bayesian variable selection to very large data sets. With a focus on examples from genome-wide association studies, we demonstrate that varbvs scales well to data sets with hundreds of thousands of variables and thousands of samples, and has features that facilitate rapid data analyses. Moreover, varbvs allows for extensive model customization, which can be used to incorporate external information into the analysis. We expect that the combination of an easy-to-use interface and robust, scalable algorithms for posterior computation will encourage broader use of Bayesian variable selection in areas of applied statistics and computational biology. The most recent R and MATLAB source code is available for download at Github (https://github.com/pcarbo/varbvs), and the R package can be installed from CRAN (https://cran.r-project.org/package=varbvs).

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Peter Carbonetto, Xiang Zhou, Matthew Stephens. 2017-09-19. varbvs: Fast Variable Selection for Large-scale Regression. https://arxiv.org/abs/1709.06597

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