Package: IHW 1.29.0

Nikos Ignatiadis

IHW: 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.

Authors:Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut]

IHW_1.29.0.tar.gz
IHW_1.29.0.zip(r-4.7)IHW_1.29.0.zip(r-4.6)IHW_1.29.0.zip(r-4.5)
IHW_1.29.0.tgz(r-4.6-any)IHW_1.29.0.tgz(r-4.5-any)
IHW_1.29.0.tar.gz(r-4.7-any)IHW_1.29.0.tar.gz(r-4.6-any)
IHW_1.29.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
IHW/json (API)

# Install 'IHW' in R:
install.packages('IHW', repos = c('https://huber-group-embl.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nignatiadis/ihw/issues

On BioConductor:IHW-1.41.0(bioc 3.24)IHW-1.40.0(bioc 3.23)

immunooncologymultiplecomparisonrnaseqihwpvalue-adjustment

8.13 score 16 stars 2 packages 403 scripts 22 exports 5 dependencies

Last updated from:2da27aa7d2. Checks:4 NOTE, 2 OK, 3 ERROR. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE203
source / vignettesOK273
linux-release-x86_64NOTE208
macos-release-arm64NOTE124
macos-oldrel-arm64NOTE143
windows-develERROR297
windows-releaseERROR163
windows-oldrelERROR299
wasm-releaseOK156

Exports:adj_pvaluesalphaas.data.framecovariate_typecovariatesget_bh_thresholdgroups_by_filtergroups_factorihwm_groupsnbinsnfoldsnrowplotpvaluesregularization_termrejected_hypothesesrejectionsshowthresholdsweighted_pvaluesweights

Dependencies:BiocGenericsfdrtoolgenericslpsymphonyslam

Introduction to Independent Hypothesis Weighting with the IHW Package

Nikos Ignatiadis, Wolfgang Huber

Rendered fromintroduction_to_ihw.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2016-10-03
Started: 2016-01-31

Readme and manuals

Help Manual

Help pageTopics
Data-driven threshold of Benjamini Hochberg Procedureget_bh_threshold
Stratify hypotheses based on increasing value of the covariategroups_by_filter
ihw: Main function for Independent Hypothesis Weightingihw ihw.default ihw.formula
ihw.DESeqResults: IHW method dispatching on DESeqResults objectsihw.DESeqResults
An S4 class to represent the ihw output.adj_pvalues adj_pvalues,ihwResult-method alpha alpha,ihwResult-method as.data.frame,ihwResult-method as.data.frame_ihwResult covariates covariates,ihwResult-method covariate_type covariate_type,ihwResult-method groups_factor groups_factor,ihwResult-method ihwResult ihwResult-class m_groups m_groups,ihwResult-method nbins nbins,ihwResult-method nfolds nfolds,ihwResult-method nrow,ihwResult-method pvalues pvalues,ihwResult-method regularization_term regularization_term,ihwResult-method rejected_hypotheses rejected_hypotheses,ihwResult-method rejections rejections,ihwResult-method show,ihwResult-method thresholds thresholds,ihwResult-method weighted_pvalues weighted_pvalues,ihwResult-method weights,ihwResult-method
Plot functions for IHWplot,ihwResult-method