Genetic algorithm are a class of optimization
    algorithms inspired by the process of natural selection and genetics.
    This package is for learning purposes and allows users to optimize
    various functions or parameters by mimicking biological evolution
    processes such as selection, crossover, and mutation. Ideal for tasks
    like machine learning parameter tuning, mathematical function
    optimization, and solving an optimization problem that involves finding 
    the best solution in a discrete space.

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.

Provides tools for plotting gene clusters and transcripts by 
  importing data from GenBank, FASTA, and GFF files. It performs BLASTP and 
  MUMmer alignments [Altschul et al. (1990) <doi:10.1016/S0022-2836(05)80360-2>;
  Delcher et al. (1999) <doi:10.1093/nar/27.11.2369>] and displays results on 
  gene arrow maps. Extensive customization options are available, including 
  legends, labels, annotations, scales, colors, tooltips, and more.

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.