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]

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)
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varbvs_2.6-10.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
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

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • cytokine - Cytokine signaling genes SNP annotation.
  • leukemia - Expression levels recorded in leukemia patients.

On CRAN:

Conda:

cpp

5.16 score 4 packages 151 scripts 378 downloads 2 mentions 34 exports 12 dependencies

Last updated from:547e829ff3. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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linux-devel-x86_64OK180
source / vignettesOK222
linux-release-arm64OK227
linux-release-x86_64OK208
macos-release-arm64OK304
macos-release-x86_64OK283
macos-oldrel-arm64OK256
macos-oldrel-x86_64OK412
windows-develOK204
windows-releaseOK200
windows-oldrelOK227
wasm-releaseOK121

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 QTLs in outbred mice using varbvs
Vignette parameters | Load the genotype and phenotype data | Fit variational approximation to posterior | Summarize the results of model fitting | Session information
Rendered fromcfw.Rmdusingknitr::rmarkdown

Last update: 2019-03-07
Started: 2017-03-24

Comparison of glmnet and varbvs in Leukemia data set
Vignette parameters | Load the Leukemia data | Fit elastic net model to data | Evaluate the glmnet predictions | Visualize results of glmnet analysis | Fit variational approximation to posterior | Evaluate the varbvs predictions | Visualize results of varbvs analysis | References | Session information
Rendered fromleukemia.Rmdusingknitr::rmarkdown

Last update: 2019-03-07
Started: 2017-03-24

Mapping disease risk loci using varbvs
Load the genotype and phenotype data | Fit variational approximation to posterior | Save the results to a file | Summarize the model fitting
Rendered fromcd.Rmdusingknitr::rmarkdown

Last update: 2017-09-08
Started: 2017-03-24

Assessing support for gene sets in disease using varbvs
Load the genotypes, phenotypes and pathway annotation | Fit variational approximation to posterior | Save the results to a file | Summarize the results of model fitting
Rendered fromcytokine.Rmdusingknitr::rmarkdown

Last update: 2017-09-08
Started: 2017-03-24