Title: Receiver Operating Characteristic Based Signature Identification
Authors@R: c(person("Xin", "Huang", role = c("aut", "cre","cph"), email = "xin.huang@abbvie.com"))
Description: Optimal linear combination predictive signatures for maximizing the area between two Receiver Operating Characteristic (ROC) curves (treatment vs. control).

Title: Aggregate Multiple ROC Curves into One Global ROC
Description: Aggregates multiple Receiver Operating Characteristic (ROC) curves
obtained from different sources into one global ROC. Additionally, it’s
also possible to calculate the aggregated precision-recall (PR) curve.
Suggests: testthat (>= 3.0.0), mockery, mockr, knitr, rmarkdown, ROCR,
Author: Pedro Mateus [aut, cre] (<https://orcid.org/0000-0002-3047-7630>)

Title: Performance Assessment of Binary Classifier with Visualization
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.
Maintainer: Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>