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keywords: classif
title: Prototypes of groups.
description: Prototypes are `representative' cases of a group of data points, given the similarity matrix among the points. They are very similar to medoids. The function is named `classCenter' to avoid conflict with the function prototype in the methods package.

keywords: classif
title: Margins of randomForest Classifier
description: Compute or plot the margin of predictions from a randomForest classifier.

keywords: classif
title: ROC curve
description: Computes the area under the receiver operating characteristics (ROC) curve for weighted and unweighted data. In addition to the area under the curve, the curve can be obtained by setting argument curve to TRUE.

keywords: classif
title: PR curve
description: Computes the area under the precision-recall (PR) curve for weighted and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the area under the curve, the curve itself can be obtained by setting argument curve to TRUE.

keywords: classif
title: Predict presence of signal peptide in protein
description: Using the hidden semi-Markov model predict presence of signal peptide in eukaryotic proteins.

keywords: classif
title: Distances between trees
description: treedist computes different tree distance methods and RF.dist the Robinson-Foulds or symmetric distance. The Robinson-Foulds distance only depends on the toplogy of the trees. If edge weights should be considered wRF.dist calculates the weighted RF distance (Robinson & Foulds 1981). and KF.dist calculates the branch score distance (Kuhner & Felsenstein 1994). path.dist computes the path difference metric as described in Steel and Penny 1993). sprdist computes the approximate SPR distance (Oliveira Martins et al. 2008, de Oliveira Martins 2016).

keywords: classif
title: Split a dataset into training and testing sets, balancing a factor
description: When performing cross-validation on a dataset, it often becomes necessary to split the data into training and test sets that are balanced for a factor. This function implements such a balanced split.