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EpipwR

Efficient Power Analysis for EWAS with Continuous or Binary Outcomes

Bioconductor version: 3.23 · Package version: 1.6.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("EpipwR")

Details

MaintainerJackson Barth <Jackson_Barth@Baylor.edu>
AuthorJackson Barth [aut, cre] (ORCID: <https://orcid.org/0009-0009-6307-9928>), Austin Reynolds [aut], Mary Lauren Benton [ctb], Carissa Fong [ctb]
LicenseArtistic-2.0
URLhttps://github.com/jbarth216/EpipwR
Bug Reportshttps://github.com/jbarth216/EpipwR
Downloads rank228
Source branchRELEASE_3_23
biocViewsEpigenetics, ExperimentalDesign, Software

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageEpipwR_1.6.0.tar.gz
Windows binary (x86_64)EpipwR_1.6.0.zip
macOS binary (arm64)EpipwR_1.6.0.tgz
macOS binary (x86_64)EpipwR_1.6.0.tgz
Dependencies

Depends: R (>= 4.4.0)

Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2

Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo