simpleFDR: Simple False Discovery Rate Calculation

Using the adjustment method from Benjamini & Hochberg (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which variables are significant under repeated testing with a given dataframe of p values and an user defined "q" threshold. It then returns the original dataframe along with a significance column where an asterisk denotes a significant p value after FDR calculation, and NA denotes all other p values. This package uses the Benjamini & Hochberg method specifically as described in Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.

Version:	1.1
Imports:	dplyr, tidyr
Published:	2021-11-04
DOI:	10.32614/CRAN.package.simpleFDR
Author:	Stephen C Wisser
Maintainer:	Stephen Wisser <swisser98 at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
CRAN checks:	simpleFDR results

Documentation:

Reference manual:

simpleFDR.pdf

Downloads:

Package source:	simpleFDR_1.1.tar.gz
Windows binaries:	r-devel: simpleFDR_1.1.zip, r-release: simpleFDR_1.1.zip, r-oldrel: simpleFDR_1.1.zip
macOS binaries:	r-devel (arm64): simpleFDR_1.1.tgz, r-release (arm64): simpleFDR_1.1.tgz, r-oldrel (arm64): simpleFDR_1.1.tgz, r-devel (x86_64): simpleFDR_1.1.tgz, r-release (x86_64): simpleFDR_1.1.tgz, r-oldrel (x86_64): simpleFDR_1.1.tgz

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