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# Independent Hypothesis Weighting 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. IHW is described in the following paper: > N. Ignatiadis, B. Klaus, J.B. Zaugg, W. Huber. *Data-driven hypothesis weighting increases detection power in genome-scale multiple testing.* Nature methods. 2016 Jul;13(7):577-80. Also see the following paper for the theoretical underpinning of the method: > N. Ignatiadis and W. Huber. *Covariate-powered cross weighted multiple testing.* [[arXiv]]( # Software availability The package is available on [Bioconductor](, and may be installed as follows: ```R if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("IHW") ``` The package can be installed as follows with `devtools` from the Github repository: ```R devtools::install_github("nignatiadis/IHW") ```