varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods

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 & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.

Package details

AuthorPeter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb]
MaintainerPeter Carbonetto <peter.carbonetto@gmail.com>
LicenseGPL (>= 3)
Version2.6-10
URL https://github.com/pcarbo/varbvs
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("varbvs")

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