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
Author: Jan Grau and Jens Keilwagen
Maintainer: Jan Grau <grau at informatik.uni-halle.de>
License: GPL-3
NeedsCompilation: no
Citation: PRROC citation info
CRAN checks: PRROC results

Downloads:

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: PRROC_1.3.1.zip, r-release: PRROC_1.3.1.zip, r-oldrel: PRROC_1.3.1.zip
OS X binaries: r-release: PRROC_1.3.1.tgz, r-oldrel: PRROC_1.3.1.tgz
Old sources: PRROC archive

Reverse dependencies:

Reverse imports: biospear, prcbench
Reverse suggests: WeightedROC

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