Package: varbvs 2.6-10
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.
Authors:
varbvs_2.6-10.tar.gz
varbvs_2.6-10.zip(r-4.7)varbvs_2.6-10.zip(r-4.6)varbvs_2.6-10.zip(r-4.5)
varbvs_2.6-10.tgz(r-4.6-x86_64)varbvs_2.6-10.tgz(r-4.6-arm64)varbvs_2.6-10.tgz(r-4.5-x86_64)varbvs_2.6-10.tgz(r-4.5-arm64)
varbvs_2.6-10.tar.gz(r-4.7-arm64)varbvs_2.6-10.tar.gz(r-4.7-x86_64)varbvs_2.6-10.tar.gz(r-4.6-arm64)varbvs_2.6-10.tar.gz(r-4.6-x86_64)
varbvs_2.6-10.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
varbvs/json (API)
| # Install 'varbvs' in R: |
| install.packages('varbvs', repos = c('https://pcarbo.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pcarbo/varbvs/issues
Last updated from:547e829ff3. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 172 | ||
| linux-devel-x86_64 | OK | 209 | ||
| source / vignettes | OK | 202 | ||
| linux-release-arm64 | OK | 195 | ||
| linux-release-x86_64 | OK | 203 | ||
| macos-release-arm64 | OK | 258 | ||
| macos-release-x86_64 | OK | 319 | ||
| macos-oldrel-arm64 | OK | 386 | ||
| macos-oldrel-x86_64 | OK | 396 | ||
| windows-devel | OK | 199 | ||
| windows-release | OK | 190 | ||
| windows-oldrel | OK | 194 | ||
| wasm-release | OK | 118 |
Exports:bayesfactorcase.names.varbvscoef.varbvscoef.varbvsmixconfint.varbvscreddeviance.varbvsfitted.varbvslabels.varbvsnobs.varbvsnormalizelogweightsplot.varbvspredict.varbvspredict.varbvsmixprint.summary.varbvsprint.varbvsrandrandnresid.varbvsresiduals.varbvssubset.varbvssummary.varbvsvar1var1.colsvarbvsvarbvsbfvarbvsbinvarbvsbinzvarbvsindepvarbvsmixvarbvsnormvarbvsproxybfvarbvspvevariable.names.varbvs
Dependencies:deldirinterpjpeglatticelatticeExtraMASSMatrixnor1mixpngRColorBrewerRcppRcppEigen
Mapping disease risk loci using varbvs
Rendered fromcd.Rmdusingknitr::rmarkdownon May 21 2026.Last update: 2017-09-08
Started: 2017-03-24
Assessing support for gene sets in disease using varbvs
Rendered fromcytokine.Rmdusingknitr::rmarkdownon May 21 2026.Last update: 2017-09-08
Started: 2017-03-24
Mapping QTLs in outbred mice using varbvs
Rendered fromcfw.Rmdusingknitr::rmarkdownon May 21 2026.Last update: 2019-03-07
Started: 2017-03-24
Comparison of glmnet and varbvs in Leukemia data set
Rendered fromleukemia.Rmdusingknitr::rmarkdownon May 21 2026.Last update: 2019-03-07
Started: 2017-03-24
