ROCket: Simple and Fast ROC Curves

A set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In 'ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) <https://www.ine.pt/revstat/pdf/rs140101.pdf> and Cai & Pepe (2004) <doi:10.1111/j.0006-341X.2004.00200.x>.

Version: 1.0.1
Imports: data.table (≥ 1.13.0)
Suggests: testthat
Published: 2021-02-17
Author: Daniel Lazar [aut, cre]
Maintainer: Daniel Lazar <da-zar at gmx.net>
BugReports: https://github.com/da-zar/ROCket/issues
License: GPL-3
URL: https://github.com/da-zar/ROCket
NeedsCompilation: no
Materials: README
CRAN checks: ROCket results

Downloads:

Reference manual: ROCket.pdf
Package source: ROCket_1.0.1.tar.gz
Windows binaries: r-devel: ROCket_1.0.1.zip, r-release: ROCket_1.0.1.zip, r-oldrel: ROCket_1.0.1.zip
macOS binaries: r-release: ROCket_1.0.1.tgz, r-oldrel: ROCket_1.0.1.tgz
Old sources: ROCket archive

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