varbvs

R package 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.


References in zbMATH (referenced in 1 article )

Showing result 1 of 1.
Sorted by year (citations)

  1. Peter Carbonetto, Xiang Zhou, Matthew Stephens: varbvs: Fast Variable Selection for Large-scale Regression (2017) arXiv