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
| Maintainer | Jackson Barth <Jackson_Barth@Baylor.edu> |
| Author | Jackson Barth [aut, cre] (ORCID: <https://orcid.org/0009-0009-6307-9928>), Austin Reynolds [aut], Mary Lauren Benton [ctb], Carissa Fong [ctb] |
| License | Artistic-2.0 |
| URL | https://github.com/jbarth216/EpipwR |
| Bug Reports | https://github.com/jbarth216/EpipwR |
| Downloads rank | 140 |
| Source branch | RELEASE_3_23 |
| biocViews | Epigenetics, ExperimentalDesign, Software |
Documentation
Download
Dependencies
Depends: R (>= 4.4.0)
Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo