ROCnReg: ROC Curve Inference with and without Covariates

Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2018) <arXiv:1806.00473>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <https://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>.

Version:	1.0
Imports:	stats, grDevices, graphics, splines, np, Matrix, moments, nor1mix, spatstat, lattice, MASS, pbivnorm
Published:	2020-03-19
Author:	Maria Xose Rodriguez-Alvarez [aut, cre], Vanda Inacio [aut]
Maintainer:	Maria Xose Rodriguez-Alvarez <mxrodriguez at bcamath.org>
License:	GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation:	no
CRAN checks:	ROCnReg results

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