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varbvs  

Large-Scale Bayesian Variable Selection Using Variational Methods
View on CRAN: Click here


Download and install varbvs package within the R console
Install from CRAN:
install.packages("varbvs")

Install from Github:
library("remotes")
install_github("cran/varbvs")

Install by package version:
library("remotes")
install_version("varbvs", "2.6-10")



Attach the package and use:
library("varbvs")
Maintained by
Peter Carbonetto
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-04-10
Latest Update: 2023-05-31
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 & 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.
How to cite:
Peter Carbonetto (2012). varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods. R package version 2.6-10, https://cran.r-project.org/web/packages/varbvs. Accessed 05 Jun. 2026.
Previous versions and publish date:
1.0 (2012-04-10 18:26), 2.0-8 (2017-03-24 06:37), 2.0.0 (2016-05-28 17:52), 2.4-0 (2017-09-08 17:03), 2.5-16 (2019-03-07 22:10), 2.6-8 (2023-03-22 20:40)
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Complete documentation for varbvs
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