Title: The Hyperdirichlet Distribution, Mark 2
Description: A suite of routines for the hyperdirichlet distribution; supersedes the hyperdirichlet package for most purposes.

Description: A suite of routines for the hyperdirichlet distribution; supersedes the 'hyperdirichlet' package for most purposes.

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

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

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

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

Imports: Rcpp (>= 1.0-7), partitions, mathjaxr, disordR (>= 0.0-9),

Description: A suite of routines for the hyperdirichlet distribution;
supersedes the 'hyperdirichlet' package.  Uses 'disordR' discipline.

Title: Hyper-Ensemble Smote Undersampled Random Forests
Author: 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.
Description: 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.
Imports: mlr, BBmisc, ParamHelpers, unbalanced, randomForest, foreach,

Imports: unbalanced, randomForest, foreach, iterators, doParallel,

Description: 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.