pROC

R package 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.


References in zbMATH (referenced in 10 articles )

Showing results 1 to 10 of 10.
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  1. Fanjul-Hevia, Arís; González-Manteiga, Wenceslao: A comparative study of methods for testing the equality of two or more ROC curves (2018)
  2. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  3. Unal, Ilker: Defining an optimal cut-point value in ROC analysis: an alternative approach (2017)
  4. Waldemar W. Koczkodaj, Alicja Wolny-Dominiak: RatingScaleReduction package: stepwise rating scale item reduction without predictability loss (2017) arXiv
  5. 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)
  6. Quintana, Fernando A.; Müller, Peter; Papoila, Ana Luisa: Cluster-specific variable selection for product partition models (2015)
  7. Mónica López-Ratón; María Rodríguez-Álvarez; Carmen Cadarso-Suárez; Francisco Gude-Sampedro: OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests (2014)
  8. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  9. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  10. 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) ioport