ROCit: Performance Assessment of Binary Classifier with Visualization

Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.

Version: 2.1.2
Imports: stats, graphics, utils, methods
Suggests: testthat, knitr, rmarkdown
Published: 2024-05-16
DOI: 10.32614/CRAN.package.ROCit
Author: Md Riaz Ahmed Khan [aut, cre], Thomas Brandenburger [aut]
Maintainer: Md Riaz Ahmed Khan <mdriazahmed.khan at>
License: GPL-3
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: ROCit results


Reference manual: ROCit.pdf
Vignettes: ROCit: An R Package for Performance Assessment of Binary Classifier with Visualization


Package source: ROCit_2.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ROCit_2.1.2.tgz, r-oldrel (arm64): ROCit_2.1.2.tgz, r-release (x86_64): ROCit_2.1.2.tgz, r-oldrel (x86_64): ROCit_2.1.2.tgz
Old sources: ROCit archive

Reverse dependencies:

Reverse imports: adjROC, animalcules, cutoff, itsdm, Rprofet, TBSignatureProfiler
Reverse suggests: DataVisualizations


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