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This is the development version of swfdr; for the stable release version, see swfdr.

Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates

Bioconductor version: Development (3.20)

This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ.

Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka

Maintainer: Simina M. Boca <smb310 at>, Jeffrey T. Leek <jtleek at>

Citation (from within R, enter citation("swfdr")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Computing covariate-adjusted q-values PDF R Script
Tutorial for swfdr package PDF R Script
Reference Manual PDF


biocViews MultipleComparison, Software, StatisticalMethod
Version 1.31.0
In Bioconductor since BioC 3.5 (R-3.4) (7 years)
License GPL (>= 3)
Depends R (>= 3.4)
Imports methods, splines, stats4, stats
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Suggests dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat
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Follow Installation instructions to use this package in your R session.

Source Package swfdr_1.31.0.tar.gz
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macOS Binary (x86_64) swfdr_1.31.0.tgz
macOS Binary (arm64) swfdr_1.31.0.tgz
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