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: |
2025.1.20 |
| Depends: |
R (≥ 4.2.0) |
| Imports: |
Rdpack, stats, mvnfast, glmmTMB, dplyr, multidplyr, ggplot2, scales |
| Suggests: |
tinytest, rmarkdown |
| Published: |
2025-01-23 |
| Author: |
Brett Klamer 
[aut, cre],
Lianbo Yu [aut] |
| Maintainer: |
Brett Klamer <code at brettklamer.com> |
| License: |
MIT + file LICENSE |
| 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 |
Documentation:
Downloads:
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