Fast Algorithms to Bootstrap Receiver Operating Characteristics Curves

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.


Reference manual

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install.packages("fbroc")

0.5.0 by Erik Peter, a month ago


https://github.com/erikpeter/fbroc


Report a bug at https://github.com/erikpeter/fbroc/issues


Browse source code at https://github.com/cran/fbroc


Authors: Erik Peter [aut, cre]


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp

Depends on ggplot2, methods, stats, utils

Linking to Rcpp


See at CRAN