varbvs-package: Large-scale Bayesian variable selection using variational...

In varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods

Large-scale Bayesian variable selection using variational methods

Description

Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto and M. Stephens, Bayesian Analysis 7, 2012, pages 73-108). This software has been applied to large data sets with over a million variables and thousands of samples.

Details

The main functionality of this package is implemented in function varbvs. This function selects the most appropriate algorithm for the data set and selected model (linear or logistic regression). See help(varbvs) for details. The varbvs interface is intended to resemble interface for glmnet, the popular package for fitting genealized linear models.

For more details about the this package, including the license and a list of available functions, see help(package=varbvs).

Author(s)

Peter Carbonetto [e-mail]

References

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

varbvs documentation built on June 7, 2023, 5:43 p.m.