A popular approach for assessing binary classifiers is analysis of their ROC curves on a set of representative data [7,8]. A ROC curve corresponds to a bidimensional plot of the sensitivity versus 1-specificity for a given classifier with continuous or ordinal output score. Two main factors have to be considered by the user when estimating the ROC curves: 1) The design of the study. Three types of dataset can be generated when pursuing a classification task: (i) paired data, where all classifiers are applied to each individual, (ii) unpaired data, where only one of the classifiers is applied to each individual, and (iii) partially-paired data [9], where the dataset is composed of both paired and unpaired data. In the case of paired and partially-paired datasets, correlation between ROC curves has to be taken into consideration. Our software is primarily designed for paired data. However, it can also analyze balanced unpaired data where the number of units is the same for each classifier. It cannot be used with partially-paired data. 2) The outcome distribution. Depending on this factor, three types of
