Package: EpipwR
Type: Package
Title: Efficient Power Analysis for EWAS with Continuous or Binary
        Outcomes
Version: 1.3.0
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
git_url: https://git.bioconductor.org/packages/EpipwR
git_branch: devel
git_last_commit: e70b2ca
git_last_commit_date: 2025-04-15
Repository: Bioconductor 3.22
Date/Publication: 2025-06-04
NeedsCompilation: no
Packaged: 2025-06-04 22:50:23 UTC; biocbuild
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>
Built: R 4.5.0; ; 2025-06-05 12:56:21 UTC; windows
