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 20 articles )

Showing results 1 to 20 of 20.
Sorted by year (citations)

  1. Jokiel-Rokita, Alicja; Topolnicki, Rafał: Estimation of the ROC curve from the Lehmann family (2020)
  2. Maria Xose Rodriguez-Alvarez, Vanda Inacio: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information (2020) arXiv
  3. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  4. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  5. Fanjul-Hevia, Arís; González-Manteiga, Wenceslao: A comparative study of methods for testing the equality of two or more ROC curves (2018)
  6. Lombarte, Mercedes; Lupo, Maela; Fina Brenda, L.; Campetelli, German; Buzalaf Marilia, A. R.; Basualdo, Marta; Rigalli, Alfredo: \textitInvivo measurement of the rate constant of liver handling of glucose and glucose uptake by insulin-dependent tissues, using a mathematical model for glucose homeostasis in diabetic rats (2018)
  7. Vivo, Juana-María; Franco, Manuel; Vicari, Donatella: Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range (2018)
  8. Krautenbacher, Norbert; Theis, Fabian J.; Fuchs, Christiane: Correcting classifiers for sample selection bias in two-phase case-control studies (2017)
  9. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  10. Sara Perez-Jaume; Konstantina Skaltsa; Natàlia Pallarès; Josep Carrasco: ThresholdROC: Optimum Threshold Estimation Tools for Continuous Diagnostic Tests in R (2017) not zbMATH
  11. Unal, Ilker: Defining an optimal cut-point value in ROC analysis: an alternative approach (2017)
  12. Waldemar W. Koczkodaj, Alicja Wolny-Dominiak: RatingScaleReduction package: stepwise rating scale item reduction without predictability loss (2017) arXiv
  13. Dincer Goksuluk, Selcuk Korkmaz, Gokmen Zararsiz, A. Ergun Karaagaoglu: easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment (2016) not zbMATH
  14. 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)
  15. Quintana, Fernando A.; Müller, Peter; Papoila, Ana Luisa: Cluster-specific variable selection for product partition models (2015)
  16. Yu, Wenbao; Park, Taesung: Two simple algorithms on linear combination of multiple biomarkers to maximize partial area under the ROC curve (2015)
  17. Mónica López-Ratón; María Rodríguez-Álvarez; Carmen Cadarso-Suárez; Francisco Gude-Sampedro: OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests (2014) not zbMATH
  18. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  19. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  20. 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