Although many examples in the existing literature about the development of new classifiers describe the use of ROC curves and their corresponding AUCs to assess their performances, the statistical significance of their differences is often not reported. This is mostly due to the lack of freely available software that is easy to use or to automate for the pairwise comparison of many binary classifiers. Albeit there are several software for performing statistical ROC analysis [19], to the best of our knowledge, the only free and readily available software for statistical ROC analysis that assesses the significance of the difference of the AUC for a pair of classifiers is ROCKIT [20,21]. This software uses maximum likelihood to fit a binormal ROC curve to the data and the statistical significance of the differences of a variety of indexes are assessed on the basis of a bivariate binormal model. In terms of usability, it has some drawbacks: 1) the input data format is rather cumbersome; 2) the output file contains many relevant data embedded in a human-readable text and thus needs to be
