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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 "EpipwR" in publications use:'
type: software
license: Artistic-2.0
title: 'EpipwR: Efficient Power Analysis for EWAS with Continuous or Binary Outcomes'
version: 1.1.0
doi: 10.1101/2024.09.06.611713
identifiers:
- type: doi
  value: 10.32614/CRAN.package.EpipwR
abstract: A quasi-simulation based approach to performing power analysis for EWAS
  (Epigenome-wide association studies) with continuous or binary outcomes. 'EpipwR'
  relies on empirical EWAS datasets to determine power at specific sample sizes while
  keeping computational cost low. EpipwR can be run with a variety of standard statistical
  tests, controlling for either a false discovery rate or a family-wise type I error
  rate.
authors:
- family-names: Barth
  given-names: Jackson
  email: Jackson_Barth@Baylor.edu
  orcid: https://orcid.org/0009-0009-6307-9928
- family-names: Reynolds
  given-names: Austin
preferred-citation:
  type: article
  title: 'EpipwR: Efficient Power Analysis for EWAS with Continuous Outcomes'
  authors:
  - family-names: Barth
    given-names: Jackson
    email: Jackson_Barth@Baylor.edu
    orcid: https://orcid.org/0009-0009-6307-9928
  - family-names: Reynolds
    given-names: Austin W.
  journal: bioRxiv
  year: '2024'
  notes: Preprint
  doi: 10.1101/2024.09.06.611713
  url: https://www.biorxiv.org/content/10.1101/2024.09.06.611713v1
repository: https://bioc.r-universe.dev
repository-code: https://github.com/jbarth216/EpipwR
commit: d393930e988f6f7e37064f8f1e1cf7d0edde8d23
url: https://github.com/jbarth216/EpipwR
contact:
- family-names: Barth
  given-names: Jackson
  email: Jackson_Barth@Baylor.edu
  orcid: https://orcid.org/0009-0009-6307-9928