pROC: display and analyze ROC curves. Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Fernandez-Lozano, Carlos; Cuiñas, Rubén F.; Seoane, José A.; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R.: Classification of signaling proteins based on molecular star graph descriptors using machine learning models (2015)
- Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
- Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
- Robin, Xavier; Turck, Natacha; Hainard, Alexandre; Tiberti, Natalia; Lisacek, Frédérique; Sanchez, Jean-Charles; Muller, Markus: Proc: an open-source package for R and S+ to analyze and compare ROC curves (2011)