depower: Power Analysis for Differential Expression Studies
Provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of independent or dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate. Negative binomial methods are described in Yu, Fernandez, and Brock (2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and Brock (2020) <doi:10.1186/s12859-020-3541-7>.
| Version: | 2025.10.21 |
| Depends: | R (≥ 4.2.0) |
| Imports: | Rdpack, stats,mvnfast,glmmTMB,dplyr,multidplyr,ggplot2,scales |
| Suggests: | tinytest,rmarkdown |
| Published: | 2025-10-22 |
| DOI: | 10.32614/CRAN.package.depower |
| Author: | Brett Klamer [aut, cre], Lianbo Yu [aut] |
| Maintainer: | Brett Klamer <code at brettklamer.com> |
| License: | MIT + fileLICENSE |
| URL: | https://brettklamer.com/work/depower/,https://bitbucket.org/bklamer/depower/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | depower citation info |
| Materials: | README,NEWS |
| CRAN checks: | depower results |
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