Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.
|Author||Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut]|
|Bioconductor views||MultipleComparison RNASeq|
|Maintainer||Nikos Ignatiadis <firstname.lastname@example.org>|
|Package repository||View on Bioconductor|
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