  Resampling algorithms are a useful approach to deal with imbalanced learning
in multilabel scenarios. These methods have to deal with singularities in the
multilabel data, such as the occurrence of frequent and infrequent labels in
the same instance. Implementations of these methods are sometimes limited to
the pseudocode provided by their authors in a paper. This Original Software
Publication presents mldr.resampling, a software package that provides
reference implementations for eleven multilabel resampling methods, with an
emphasis on efficiency since these algorithms are usually time-consuming.
