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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "IHW" in publications use:'
type: software
license: Artistic-2.0
title: 'IHW: Independent Hypothesis Weighting'
version: 1.33.0
doi: 10.1038/nmeth.3885
abstract: 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.
authors:
- family-names: Ignatiadis
  given-names: Nikos
  email: nikos.ignatiadis01@gmail.com
- family-names: Huber
  given-names: Wolfgang
preferred-citation:
  type: article
  title: Data-driven hypothesis weighting increases detection power in genome-scale
    multiple testing
  authors:
  - family-names: Ignatiadis
    given-names: Nikolaos
  - family-names: Klaus
    given-names: Bernd
  - family-names: Zaugg
    given-names: Judith
  - family-names: Huber
    given-names: Wolfgang
  year: '2016'
  journal: Nature Methods
  doi: 10.1038/nmeth.3885
repository: https://bioc.r-universe.dev
contact:
- family-names: Ignatiadis
  given-names: Nikos
  email: nikos.ignatiadis01@gmail.com
references:
- type: article
  title: Covariate-powered weighted multiple testing with false discovery rate control
  authors:
  - family-names: Ignatiadis
    given-names: Nikolaos
  - family-names: Huber
    given-names: Wolfgang
  year: '2017'
  journal: arXiv
  doi: arXiv:1701.05179