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:Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb]

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varbvs.pdf |varbvs.html✨
varbvs/json (API)

Peer review:

Bug tracker:https://github.com/pcarbo/varbvs/issues

On CRAN:

34 exports 1.24 score 12 dependencies 2 dependents 2 mentions 130 scripts 334 downloads

Last updated 1 years agofrom:547e829ff3. Checks:OK: 9. Indexed: yes.

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