For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.

Multi-category angle-based large-margin classifiers.
    See Zhang and Liu (2014) <doi:10.1093/biomet/asu017> for details.

An implementation of Karaboga (2005) Artificial Bee Colony
    Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>.
    This (working) version is a Work-in-progress, which is
    why it has been implemented using pure R code. This was developed upon the basic
    version programmed in C and distributed at the algorithm's official website.

Tests the goodness of fit of a distribution of offspring to the Normal, Poisson, and Gamma distribution and estimates the proportional paternity of the second male (P2) based on the best fit distribution.

Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.
  Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) <doi:10.1093/bioinformatics/btv684>.
  Estoup A., Raynal L., Verdu P. and Marin J.-M. <http://journal-sfds.fr/article/view/709>.
  Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) <doi:10.1093/bioinformatics/bty867>.