Large-scale Bayesian variable selection for R and MATLAB.
R Matlab C C++

Large-scale Bayesian variable selection for R and MATLAB

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We introduce varbvs, a suite of functions writen in R and MATLAB for analysis of large-scale data sets using Bayesian variable selection methods. To facilitate application of Bayesian variable selection to a range of problems, the varbvs interface hides most of the complexities of modeling and optimization, while also providing many options for adaptation to range of applications. The varbvs software has been used to implement Bayesian variable selection for large problems with over a million variables and thousands of samples, including analysis of massive genome-wide data sets.

For more details on the R package, see the README in the varbvs-R subdirectory. For example, only a few lines of R code are needed to fit a variable selection model to the Leukemia data:

fit <- varbvs(leukemia$x,NULL,leukemia$y,family = "binomial",
              logodds = seq(-3.5,-1,0.1),sa = 1)

For more details on the MATLAB interface, see the README in the varbvs-MATLAB subdirectory. The MATLAB package also provides a simple interface for fitting variable selection models; this is the call to varbvs from the demo_qtl.m example:

fit = varbvs(X,Z,y,labels,[],struct('logodds',-3:0.1:-1));

Citing varbvs

If you find that this software is useful for your research project, please cite our paper:

Carbonetto, P., and Stephens, M. (2012). Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Analysis 7, 73-108.


Copyright (c) 2012-2017, Peter Carbonetto.

The varbvs source code repository by Peter Carbonetto is free software: you can redistribute it under the terms of the GNU General Public License. All the files in this project are part of varbvs. This project is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See file LICENSE for the full text of the license.


The varbvs software package was developed by:
Peter Carbonetto
Dept. of Human Genetics, University of Chicago

Xiang Zhou, Xiang Zhu, David Gerard and Matthew Stephens have also contributed to the development of this software.