Classes and methods for handling genetic data. Includes
        classes to represent genotypes and haplotypes at single markers
        up to multiple markers on multiple chromosomes. Function
        include allele frequencies, flagging homo/heterozygotes,
        flagging carriers of certain alleles, estimating and testing
        for Hardy-Weinberg disequilibrium, estimating and testing for
        linkage disequilibrium, ...

Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.

Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.

Triangular and trapezoidal fuzzy numbers are used to study fuzzy logic,
    fuzzy reasoning and approximating, fuzzy regression models, etc.
    This package builds the generating function for triangular and trapezoidal fuzzy numbers based on Souliotis et al. (2022)<doi:10.3390/math10183350>. 
    They proposed a method for the construction of fuzzy numbers via a cumulative distribution function based on the possibility theory.

Includes the reference implementation of Genie - a hierarchical
    clustering algorithm that links two point groups in such a way that
    an inequity measure (namely, the Gini index) of the cluster sizes
    does not significantly increase above a given threshold.
    This method most often outperforms many other data segmentation approaches
    in terms of clustering quality as tested on a wide range of benchmark
    datasets. At the same time, Genie retains the high speed of the single
    linkage approach, therefore it is also suitable for analysing larger data sets.
    For more details see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>).
    For an even faster and more feature-rich implementation, including,
    amongst others, noise point detection, see the 'genieclust' package.