This is the main summa function, which estimates a ranking of a given number of methods in an unsupervised setting. It also constructs an unsupervised ensemble of the given methods. If type is "binary" we implement the SML method by Parisi et. al. for more detail see [1,2].
A matrix of size samples by methods. If type="binary", this should consist of binary values. By convention samples belonging to the negative class should be denoted by -1 and to the positive denoted by 1. If type="rank" each entry corresponds to a confidence score (not necessarily ranked by the user) with samples having higher scores are more likely to belong to the positive class
A character specifying the nature of the analysis and can be either "binary" or "rank".
An object of class summa
 F. Parisi et. al. "Ranking and combining multiple predictors without labeled data". doi:10.1073, Proceedings of the National Academy of Sciences, 2014.
 A. Jaffe et. al. "Estimating the accuracies of multiple classifiers without labeled data. In AISTATS (Vol. 2,p. 4).
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.