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 33 articles )

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  1. Al-Mekhlafi, Amani; Becker, Tobias; Klawonn, Frank: Sample size and performance estimation for biomarker combinations based on pilot studies with small sample sizes (2022)
  2. Gneiting, Tilmann; Vogel, Peter: Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation (2022)
  3. Jun Woo, Jinhua Wang: bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R (2022) not zbMATH
  4. Christian Thiele; Gerrit Hirschfeld: cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R (2021) not zbMATH
  5. Kandanaarachchi, Sevvandi; Hyndman, Rob J.: Dimension reduction for outlier detection using DOBIN (2021)
  6. Vishwakarma, Gajendra K.; Thomas, Abin; Bhattacharjee, Atanu: A weight function method for selection of proteins to predict an outcome using protein expression data (2021)
  7. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2021)
  8. Díaz-Coto, Susana; Martínez-Camblor, Pablo; Corral-Blanco, Norberto Octavio: Cumulative/dynamic ROC curve estimation under interval censorship (2020)
  9. Díaz-Coto, Susana; Martínez-Camblor, Pablo; Pérez-Fernández, Sonia: SmoothROCtime: an (\mathsfR) package for time-dependent ROC curve estimation (2020)
  10. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  11. Jokiel-Rokita, Alicja; Topolnicki, Rafał: Estimation of the ROC curve from the Lehmann family (2020)
  12. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  13. Maria Xose Rodriguez-Alvarez, Vanda Inacio: ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information (2020) arXiv
  14. Muschelli, John III: ROC and AUC with a binary predictor: a potentially misleading metric (2020)
  15. Julien Chiquet, Pierre Barbillon, Timothée Tabouy: missSBM: An R Package for Handling Missing Values in the Stochastic Block Model (2019) arXiv
  16. Wang, Wan-Lun: Mixture of multivariate (t) nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values (2019)
  17. Fanjul-Hevia, Arís; González-Manteiga, Wenceslao: A comparative study of methods for testing the equality of two or more ROC curves (2018)
  18. 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)
  19. Vivo, Juana-María; Franco, Manuel; Vicari, Donatella: Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range (2018)
  20. Krautenbacher, Norbert; Theis, Fabian J.; Fuchs, Christiane: Correcting classifiers for sample selection bias in two-phase case-control studies (2017)

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