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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.6-10 |
rolling linux/jammy R-4.5 | varbvs_2.6-10.tar.gz |
2.3 MiB |
2.6-10 |
rolling linux/noble R-4.5 | varbvs_2.6-10.tar.gz |
2.3 MiB |
2.6-10 |
rolling source/ R- | varbvs_2.6-10.tar.gz |
2.2 MiB |
2.6-10 |
latest linux/jammy R-4.5 | varbvs_2.6-10.tar.gz |
2.3 MiB |
2.6-10 |
latest linux/noble R-4.5 | varbvs_2.6-10.tar.gz |
2.3 MiB |
2.6-10 |
latest source/ R- | varbvs_2.6-10.tar.gz |
2.2 MiB |
2.6-10 |
2026-04-26 source/ R- | varbvs_2.6-10.tar.gz |
2.2 MiB |
2.6-10 |
2026-04-23 source/ R- | varbvs_2.6-10.tar.gz |
2.2 MiB |
2.6-10 |
2026-04-09 windows/windows R-4.5 | varbvs_2.6-10.zip |
2.7 MiB |
2.6-10 |
2025-04-20 source/ R- | varbvs_2.6-10.tar.gz |
2.2 MiB |