@@ 1529,6 1529,35 @@ allows transformation of geographic coordinates from one projection and/or
datum to another.")
(license license:gpl2)))
+(define-public r-rocit
+ (package
+ (name "r-rocit")
+ (version "2.1.1")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (cran-uri "ROCit" version))
+ (sha256
+ (base32 "0sd6ckh7k8aqwhzzp3qff6g7d03klbr0mbp403pib3823c8pqa55"))))
+ (properties `((upstream-name . "ROCit")))
+ (build-system r-build-system)
+ (native-inputs (list r-knitr))
+ (home-page "https://cran.r-project.org/package=ROCit")
+ (synopsis "Performance Assessment of Binary Classifier with Visualization")
+ (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 classifiers for certain thresholds. These
+metrics are calculated at certain threshold values. @dfn{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. The
+ROCit package provides flexibility to easily evaluate threshold-bound
+metrics.")
+ (license license:gpl3)))
+
(define-public r-rorcid
(package
(name "r-rorcid")