Fast Algorithms to Bootstrap Receiver Operating Characteristics Curves
Bootstrap paired ROC curves
Bootstrap ROC curve
Process bootstrapped TPR/FPR at thresholds matrix into TPR at FPR matr...
Generates confidence intervals for the difference in TPR between two p...
Generates confidence intervals for the TPR for a range of FPRs or vice...
Generic S3 function to calculate confidence regions for ROC curves
Extracts one from two paired ROC curves from a fbroc.paired.roc obje...
fbroc: A package for fast bootstrap analysis and comparison of ROC cur...
Calculate performance for paired bootstrapped ROC curves
Calculate performance for bootstrapped ROC curve
Generic S3 function to calculate performance estimates for ROC curves
Plots function for object of class fbroc.conf.paired
Plots function for object of classfbroc.conf
Plots a fbroc.paired.roc object
Plots the difference between the bootstrapped performance estimate of ...
Plots ROC based performance metric as histogram
Plots a fbroc.roc object
Prints information about a fbroc.perf.paired object
Prints information about a fbroc.perf object
Prints information about a fbroc.roc object
Implements a very fast C++ algorithm to quickly bootstrap receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the curve (AUC) and the partial area under the curve as well as the true and false positive rate. The analysis of paired receiver operating curves is supported as well, so that a comparison of two predictors is possible. You can also plot the results and calculate confidence intervals. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second.
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