Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces.

Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.

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

Aggregate Multiple ROC Curves into One Global ROC
person(given = "Pedro",
family = "Mateus",
email = "pedro-cm@live.com.pt",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-3047-7630"))
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.
testthat (>= 3.0.0), mockery, mockr, knitr, rmarkdown, ROCR,
pROC, pracma, stats

Performance Assessment of Binary Classifier with Visualization
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" ))
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.
Md Riaz Ahmed Khan [aut, cre],
Thomas Brandenburger [aut]
Md Riaz Ahmed Khan <mdriazahmed.khan@jacks.sdstate.edu>