Package: ROCit
Language: en-US
Type: Package
Title: Performance Assessment of Binary Classifier with Visualization
Version: 2.1.2
Date: 2024-05-14
Authors@R: c(person("Md Riaz Ahmed", "Khan", email = "mdriazahmed.khan@jacks.sdstate.edu", role = c("aut", "cre")),
    person("Thomas", "Brandenburger", email = "thomas.brandenburger@sdstate.edu", role = "aut" ))
Description: 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. 
Imports: stats, graphics, utils, methods
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-05-16 14:09:53 UTC; riaza
Author: Md Riaz Ahmed Khan [aut, cre],
  Thomas Brandenburger [aut]
Maintainer: Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>
Repository: CRAN
Date/Publication: 2024-05-16 14:30:02 UTC
Built: R 4.6.0; ; 2026-04-26 21:24:34 UTC; unix
