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 25 articles )

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  1. Vishwakarma, Gajendra K.; Thomas, Abin; Bhattacharjee, Atanu: A weight function method for selection of proteins to predict an outcome using protein expression data (2021)
  2. Díaz-Coto, Susana; Martínez-Camblor, Pablo; Pérez-Fernández, Sonia: SmoothROCtime: an (\mathsfR) package for time-dependent ROC curve estimation (2020)
  3. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  4. Jokiel-Rokita, Alicja; Topolnicki, Rafał: Estimation of the ROC curve from the Lehmann family (2020)
  5. Maria Xose Rodriguez-Alvarez, Vanda Inacio: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information (2020) arXiv
  6. Muschelli, John III: ROC and AUC with a binary predictor: a potentially misleading metric (2020)
  7. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  8. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  9. Fanjul-Hevia, Arís; González-Manteiga, Wenceslao: A comparative study of methods for testing the equality of two or more ROC curves (2018)
  10. 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)
  11. Vivo, Juana-María; Franco, Manuel; Vicari, Donatella: Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range (2018)
  12. Krautenbacher, Norbert; Theis, Fabian J.; Fuchs, Christiane: Correcting classifiers for sample selection bias in two-phase case-control studies (2017)
  13. Matthew Dixon, Diego Klabjan, Lan Wei: OSTSC: Over Sampling for Time Series Classification in R (2017) arXiv
  14. Michael C. Sachs: plotROC: A Tool for Plotting ROC Curves (2017) not zbMATH
  15. Sara Perez-Jaume; Konstantina Skaltsa; Natàlia Pallarès; Josep Carrasco: ThresholdROC: Optimum Threshold Estimation Tools for Continuous Diagnostic Tests in R (2017) not zbMATH
  16. Unal, Ilker: Defining an optimal cut-point value in ROC analysis: an alternative approach (2017)
  17. Waldemar W. Koczkodaj, Alicja Wolny-Dominiak: RatingScaleReduction package: stepwise rating scale item reduction without predictability loss (2017) arXiv
  18. 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
  19. 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)
  20. Quintana, Fernando A.; Müller, Peter; Papoila, Ana Luisa: Cluster-specific variable selection for product partition models (2015)

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