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swfdr

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

Bioconductor version: 3.24 · Package version: 1.39.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("swfdr")

Details

MaintainerSimina M. Boca <smb310@georgetown.edu>, Jeffrey T. Leek <jtleek@gmail.com>
AuthorJeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka
LicenseGPL (>= 3)
URLhttps://github.com/leekgroup/swfdr
Bug Reportshttps://github.com/leekgroup/swfdr/issues
Downloads rank466
Source branchdevel
biocViewsMultipleComparison, Software, StatisticalMethod

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packageswfdr_1.39.0.tar.gz
Windows binary (x86_64)swfdr_1.39.0.zip
macOS binary (arm64)swfdr_1.39.0.tgz
macOS binary (x86_64)swfdr_1.39.0.tgz
Dependencies

Depends: R (>= 3.4)

Imports: methods, splines, stats4, stats

Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat