ROCModels

Help for package ROCModels const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {ROCModels} Contents ROCModels AUC DMDmodified Title: ROC Models and AUC Estimation Version: 1.0.0 Description: The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. 'ROCModels' helps researchers identify and implement ROC inference methods most suitable for their data. 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(2002) < doi:10.1198/106186002411 >, Jokiel-Rokita, A., and Topolnicki, R. (2020) < doi:10.1016/j.csda.2019.106820 >, Krzanowski, W. J., and Hand, D. J. (2009) < doi:10.1201/9781439800225 >, Kundu, D., and Gupta, R. D. (2006) < doi:10.1109/TR.2006.874918 >, Lloyd, C. J. (1998) < doi:10.1080/01621459.1998.10473797 >, Lehmann, E. L. (1953) < doi:10.1214/aoms/1177729080 >, Metz, C. E., Herman, B. A., and Shen, J. H. (1998) < doi:10.1002/(SICI)1097-0258(19980515)17:9%3C1033::AID-SIM784%3E3.0.CO;2-Z >, Pepe, M. S. (2003) < doi:10.1093/oso/9780198509844.001.0001 >, Pundir, S., and Amala, R. (2014) < doi:10.22237/jmasm/1398917940 >, Silverman, B. W. (2018) < doi:10.1201/9781315140919 >, Yeo, I. K., and Johnson, R. A. (2000) < doi:10.1093/biomet/87.4.954 >, Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) < doi:10.1002/9780470906514 >, Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) < doi:10.1002/(SICI)1097-0258(19971015)16:19%3C2143::AID-SIM655%3E3.0.CO;2-3 >. License: MIT + file LICENSE Encoding: UTF-8 Imports: ggplot2, kedd, dplyr, survival, nleqslv, HDInterval, ROCit, doParallel, foreach, pbivnorm, nor1mix, parallel, readr, MASS, doRNG Depends: R (≥ 3.5) LazyData: true Suggests: knitr, rmarkdown VignetteBuilder: knitr RoxygenNote: 7.3.2 NeedsCompilation: no Packaged: 2026-03-11 18:31:53 UTC; rsn11 Author: Ruhul Ali Khan [aut], Ruhul Ali Khan [aut, cre], Raja Sanjeev Kumar Nakka [aut], Musie Ghebremichael [aut] Maintainer: Ruhul Ali Khan <ruhulali.khan@gmail.com> Repository: CRAN Date/Publication: 2026-03-16 19:50:13 UTC ROCModels: Tools for ROC Curve Analysis Description The ROCModels package provides functions for calculating AUC, generating ROC plots, and comparing classification models. Vignettes See the package vignette for a detailed introduction and examples: vignette("ROCModels_Package_Doc") You can also open all available vignettes with: browseVignettes("ROCModels") Author(s) Maintainer : Ruhul Ali Khan ruhulali.khan@gmail.com Authors: Ruhul Ali Khan Raja Sanjeev Kumar Nakka Musie Ghebremichael musie_ghebremichael@dfci.harvard.edu Calculates AUC, confidence intervals, and generates a ROC plot. Description Calculates AUC, confidence intervals, and generates a ROC plot. Usage AUC( data, method, ci = TRUE, ci_method = "delong", siglevel = 0.05, boot_iter = 1000, seed = NULL ) Arguments data A data frame containing at least two columns: biomarker Numeric values representing the diagnostic marker. status Character or factor with levels '"0"' (controls) and '"1"' (cases). method A character string specifying the ROC/AUC modeling approach. Supported options include: '"empirical"' – empirical ROC '"order"' – ROC curve under stochastic order constraints '"norm_silver"' – kernel ROC with normal kernel and Silverman bandwidth '"norm_ucv"' – kernel ROC with normal kernel and UCV bandwidth '"bi_silver"' – kernel ROC with biweight kernel and Silverman bandwidth '"bi_ucv"' – kernel ROC with biweight kernel and UCV bandwidth '"binormal"' – classical binormal ROC model '"biweibull"' – parametric bi-Weibull ROC '"bigamma"' – parametric ROC assuming gamma distributions '"lehmann"' – ROC under the Lehmann alternative '"bayesbiweibull"' – Bayesian bi-Weibull ROC (MCMC-based) '"BB"' – Bayesian bootstrap ROC '"dpm"' – Dirichlet process mixture ROC ci Logical; if 'TRUE' (default), computes confidence intervals for the AUC (or credible intervals for Bayesian methods). ci_method Character string specifying the type of interval estimation. Not all CI methods are compatible with every model: '"delong"' – DeLong’s variance-based normal approximation '"bootstrap"' – nonparametric bootstrap interval '"hm"' – Hanley–McNeil variance-based interval '"mle"' – likelihood-based interval '"all"' – computes all applicable interval types for the selected method siglevel Numeric; significance level \alpha for the confidence interval. The corresponding confidence level is 1 - \alpha . boot_iter Integer; number of bootstrap resamples (used when 'ci_method = "bootstrap"' or '"all"'). Larger values give more stable intervals but increase computation time. seed Integer; random seed for reproducibility. Value A list with the following elements: summary Printed output of the AUC and confidence intervals. plot A 'ggplot' object visualizing the ROC curve. The exact structure may vary depending on the chosen model. Examples # Import well formated dataset data(DMDmodified) # Calculate AUC summary and ROC plot auc <- AUC( data=DMDmodified, method = "empirical", ci = TRUE ) # Get the AUC summary cat(auc$summary) # Get the ROC plot auc$plot DMDmodified dataset Description A dataset used for ROC modeling examples. Usage DMDmodified Format A data frame with X rows and Y variables: X ID for the row biomarker Biomarker value status Status