Implementation of the Artificial Hydrocarbon Networks for data
    modeling.

Provide fast multi-pattern string matching for 'R' using the
    'Aho-Corasick' algorithm, powered by the 'Rust' 'aho-corasick' crate. It
    builds reusable automatons for detecting matches, counting matches, locating
    character, extracting matched text, and replacing matches in character vectors.
    For more details on the 'Aho-Corasick' algorithm, please see
    Aho and Corasick (1975) <doi:10.1145/360825.360855>.

Aho-Corasick is an optimal algorithm for finding many
    keywords in a text. It can locate all matches in a text in O(N+M) time; i.e.,
    the time needed scales linearly with the number of keywords (N) and the size of
    the text (M). Compare this to the naive approach which takes O(N*M) time to loop
    through each pattern and scan for it in the text. This implementation builds the
    trie (the generic name of the data structure) and runs the search in a single
    function call. If you want to search multiple texts with the same trie, the
    function will take a list or vector of texts and return a list of matches to
    each text. By default, all 128 ASCII characters are allowed in both the keywords
    and the text. A more efficient trie is possible if the alphabet size can be
    reduced. For example, DNA sequences use at most 19 distinct characters and
    usually only 4; protein sequences use at most 26 distinct characters and usually
    only 20. UTF-8 (Unicode) matching is not currently supported.

Implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) 
    to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing 
    the results and generating graphical outputs. Method as described in: 
    dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.

The AHP method (Analytic Hierarchy Process) is a multi-criteria decision-making method addressing
             choice and outranking problems. The method enables to perform the analysis of alternatives in each
             type of criterion and then provides a global performance of each alternative in the decision context.
             The main difference of this package is the possibility of evaluating the alternatives using quantitative
             data, by numerical representation, and qualitative data, using the Saaty scale, providing preference
             relation between variables by a pairwise evaluation.