R (>= 2.10), methods, magrittr, cubature

A suite of routines for the hyperdirichlet distribution; supersedes the 'hyperdirichlet' package.

methods, magrittr, cubature, R (>= 3.5.0)
Rcpp (>= 0.12.5), partitions, EMC, mathjaxr

knitr, markdown, rmarkdown, testthat, bookdown, rticles
Rcpp (>= 1.0-7), partitions, EMC, mathjaxr, disordR (>= 0.0-8)

Rcpp (>= 1.0-7), partitions, mathjaxr, disordR (>= 0.0-9),
alabama, calibrator

A suite of routines for the hyperdirichlet distribution;
supersedes the 'hyperdirichlet' package.  Uses 'disordR' discipline.
Rcpp (>= 1.0-7), partitions, mathjaxr, disordR (>= 0.0-9),
alabama, calibrator, Rdpack

Hyper-Ensemble Smote Undersampled Random Forests
Giorgio Valentini [aut, cre] - AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano;
Max Schubach [ctb] - Charite, Universitatsmedizin Berlin;
Matteo Re [ctb] - AnacletoLab, Dipartimento di Informatica, Universita' degli Studi di Milano;
Peter N Robinson [ctb] - The Jackson Laboratory for Genomic Medicine, Farmington CT, USA.
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data. Both single-core and parallel multi-core version of hyperSMURF are implemented.
mlr, BBmisc, ParamHelpers, unbalanced, randomForest, foreach,
iterators, doParallel, parallel

unbalanced, randomForest, foreach, iterators, doParallel,
parallel

Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data.