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

Documentation:

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

Downloads:

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
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, FRASER, GroupBN, HPiP, ICBioMark, mlr3measures, MSiP, OUTRIDER, prcbench, preciseTAD, SIAMCAT, simtrait
Reverse suggests:	pareg, PheVis, WeightedROC

Linking:

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