Exploratory data analysis and manipulation functions for multi-
    label data sets along with an interactive Shiny application to ease their use.

Large collection of multilabel datasets along with the functions
    needed to export them to several formats, to make partitions, and to obtain
    bibliographic information.

Collection of the state of the art multi-label resampling algorithms. The objective of these algorithms is to achieve balance in multi-label datasets.

Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.

Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" <ISBN:978-0-471-58495-7>, Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". <ISBN:978-0-471-71580-1> and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". <ISBN:978-0-471-95333-3>.

Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.

A unified interface is provided to various machine learning
  algorithms like linear or quadratic discriminant analysis, k-nearest
  neighbors, random forest, support vector machine, ... It allows to train,
  test, and apply cross-validation using similar functions and function
  arguments with a minimalist and clean, formula-based interface. Missing data
  are processed the same way as base and stats R functions for all algorithms,
  both in training and testing. Confusion matrices are also provided with a rich
  set of metrics calculated and a few specific plots.