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 jacks.sdstate.edu>
License:	GPL-3
NeedsCompilation:	no
Language:	en-US
Materials:	README NEWS
CRAN checks:	ROCit results

Documentation:

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

Downloads:

Package source:	ROCit_2.1.2.tar.gz
Windows binaries:	r-devel: ROCit_2.1.2.zip, r-release: ROCit_2.1.2.zip, r-oldrel: ROCit_2.1.2.zip
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
Reverse suggests:	DataVisualizations

Linking:

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