Description: Implementation of topological data analysis methods based on 
    graph-theoretic approaches for discovering topological structures in data.
    The core algorithm constructs topological spaces from graphs following
    Nada et al. (2018) <doi:10.1002/mma.5096> "New types of topological 
    structures via graphs".

Deciphering hierarchy of agents exhibiting observable dominance events is a crucial problem in several disciplines, in particular in behavioural analysis of social animals, but also in social sciences and game theory. This package implements an inference approach based on graph theory, namely to extract the optimal acyclic subset of a weighted graph of dominance; this allows for hierarchy estimation through topological sorting. The package also contains infrastructure to investigate partially defined hierarchies and hierarchy dynamics.

Flexible and ergonomic topological sorting implementation for
    R. Supports a variety of input data encoding (lists of edges or
    adjacency matrices, graphs edge direction), stable sort variants as
    well as cycle detection with detailed diagnosis.

A collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.

Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the 'topr' package.

Evaluation of alternatives based on multiple criteria using TOPSIS method.

Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.