PRROC: Precision-Recall and ROC Curves for Weighted and Unweighted Data

Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.

Version: 1.3.1
Suggests: testthat, ggplot2, ROCR
Published: 2018-06-19
DOI: 10.32614/CRAN.package.PRROC
Author: Jan Grau and Jens Keilwagen
Maintainer: Jan Grau <grau at>
License: GPL-3
NeedsCompilation: no
Citation: PRROC citation info
CRAN checks: PRROC results


Reference manual: PRROC.pdf
Vignettes: PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R


Package source: PRROC_1.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): PRROC_1.3.1.tgz, r-oldrel (arm64): PRROC_1.3.1.tgz, r-release (x86_64): PRROC_1.3.1.tgz, r-oldrel (x86_64): PRROC_1.3.1.tgz
Old sources: PRROC archive

Reverse dependencies:

Reverse imports: biospear, DeepPINCS, DeProViR, FRASER, GroupBN, HPiP, ICBioMark, mlr3measures, MSiP, OUTRIDER, prcbench, preciseTAD, saseR, SIAMCAT, simtrait, usefun
Reverse suggests: pareg, PheVis, WeightedROC


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