Package: EpipwR
Type: Package
Title: Efficient Power Analysis for EWAS with Continuous or Binary
        Outcomes
Version: 1.7.0
Authors@R: 
  c(person(given="Jackson", family="Barth", role=c("aut","cre"), 
  email="Jackson_Barth@Baylor.edu",
  comment=c(ORCID = "0009-0009-6307-9928")),
    person("Austin", "Reynolds", role="aut"),
    person("Mary Lauren", "Benton", role="ctb"),
    person("Carissa","Fong",role="ctb"))
Description: 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.
License: Artistic-2.0
Encoding: UTF-8
URL: https://github.com/jbarth216/EpipwR
BugReports: https://github.com/jbarth216/EpipwR
Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2
Depends: R (>= 4.4.0)
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo
VignetteBuilder: knitr
biocViews: Epigenetics, ExperimentalDesign
Config/testthat/edition: 3
Config/pak/sysreqs: libicu-dev libpng-dev libssl-dev zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-04-28 13:03:47 UTC
RemoteUrl: https://github.com/bioc/EpipwR
RemoteRef: HEAD
RemoteSha: 9d8d4c28f74250813dd5ee5a44c4a02e2e7d3084
NeedsCompilation: no
Packaged: 2026-07-03 18:29:56 UTC; root
Author: Jackson Barth [aut, cre] (ORCID:
    <https://orcid.org/0009-0009-6307-9928>),
  Austin Reynolds [aut],
  Mary Lauren Benton [ctb],
  Carissa Fong [ctb]
Maintainer: Jackson Barth <Jackson_Barth@Baylor.edu>