depower
2026.1.30Power Analysis for Differential Expression Studies
Overview
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>.
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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code
Code & tests
Open call graph →Line coverage
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Tests / Examples
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Functions
100 27 exported
Complexity
7.4 avg / 30 max
Call network
100 nodes / 148 edges
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4 releases. Pick two to compare their code metrics; R releases are shown for context.
Package metadata
- First published
- 2024-12-08
- Total releases
- 4 / 2 yrs
- License
- MIT + file LICENSE OSI
- Download size
- not tracked yet
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- With dependencies
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