DOI: 10.18129/B9.bioc.swfdr  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see swfdr.

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

Bioconductor version: 3.17

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 georgetown.edu>, Jeffrey T. Leek <jtleek at gmail.com>

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


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PDF R Script Computing covariate-adjusted q-values
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biocViews MultipleComparison, Software, StatisticalMethod
Version 1.26.0
In Bioconductor since BioC 3.5 (R-3.4) (6.5 years)
License GPL (>= 3)
Depends R (>= 3.4)
Imports methods, splines, stats4, stats
Suggests dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat
URL https://github.com/leekgroup/swfdr
BugReports https://github.com/leekgroup/swfdr/issues
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Source Package swfdr_1.26.0.tar.gz
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