Package: ROCit
Language: en-US
Type: Package
Title: Performance Assessment of Binary Classifier with Visualization
Version: 2.1.1
Date: 2020-06-14
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: 7.1.0
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-06-10 09:00:25 UTC; root
Author: Md Riaz Ahmed Khan [aut, cre], Thomas Brandenburger [aut]
Maintainer: Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>
Repository: https://riazakhan94.r-universe.dev
Date/Publication: 2022-05-19 02:01:12 UTC
RemoteUrl: https://github.com/riazakhan94/rocit
RemoteRef: HEAD
RemoteSha: 068953e58f5e46291c033a82b57e9e42fe30806d