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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "varbvs" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods'
version: 2.6-10
doi: 10.1214/12-BA703
identifiers:
- type: doi
  value: 10.32614/CRAN.package.varbvs
abstract: 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:
- family-names: Carbonetto
  given-names: Peter
  email: peter.carbonetto@gmail.com
- family-names: Stephens
  given-names: Matthew
preferred-citation:
  type: article
  title: Scalable variational inference for Bayesian variable selection in regression,
    and its accuracy in genetic association studies
  authors:
  - family-names: Carbonetto
    given-names: Peter
    email: peter.carbonetto@gmail.com
  - family-names: Stephens
    given-names: Matthew
  journal: Bayesian Analysis
  volume: '7'
  issue: '1'
  year: '2012'
  doi: 10.1214/12-BA703
  start: '73'
  end: '108'
repository: https://pcarbo.r-universe.dev
repository-code: https://github.com/pcarbo/varbvs
commit: 547e829ff36af1e22f26eadad4b3615ac9a9d062
url: https://github.com/pcarbo/varbvs
date-released: '2023-05-31'
contact:
- family-names: Carbonetto
  given-names: Peter
  email: peter.carbonetto@gmail.com