movieROC - Visualization for Classification Regions for ROC Curve Generalization

Tools for estimating the Receiver Operating Characteristic (ROC) curve under different generalizations: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC curve); - transforming the marker by a function either defined by the user or resulting from a logistic regression model (hROC curve); - considering a linear transformation with some fixed parameters introduced by the user, dynamic parameters resulting from Meisner et al. (2017) approach or empirically maximizing TPR for each FPR for a bivariate marker. Also a quadratic transformation with particular coefficients or a function fitted by a logistic regression model can be considered (biROC curve); - considering a linear transformation with some fixed parameters introduced by the user, dynamic parameters resulting from Meisner et al. (2017) approach or a function fitted by a logistic regression model (multiROC curve). The classification regions behind each point of the ROC curve are displayed in both fixed graphics (plot.buildROC, plot.regions or plot.funregions function) or videos (movieROC function).