Fits and analyzes simplex regression models with either
    fixed or parametric mean link functions. Implements the simplex
    probability density function, cumulative distribution function,
    quantile function, random number generation, and variance evaluation.
    Offers several fixed and parametric link functions for the mean
    submodel, tools for residual analysis and diagnostic plotting,
    hypothesis testing procedures, and influence measures such as Cook's
    distance and leverage (hat values). Includes the Scout Score (SS)
    criterion for model selection, enabling comprehensive inference and
    diagnostic analysis within the simplex regression framework.
    For more details see Barndorff-Nielsen and Jorgensen (1991)
    <doi:10.1016/0047-259X(91)90008-P> and Justino and Cribari-Neto (2026) 
    <doi:10.1016/j.apm.2025.116713>.

Identifies constant, additive, multiplicative, and user-defined 
    simplivariate components in numeric data matrices using a genetic algorithm. 
    Supports flexible pattern definitions and provides visualization for 
    general biclustering applications across diverse domains. The method builds 
    on simplivariate models as introduced in Hageman et al. (2008) 
    <doi:10.1371/journal.pone.0003259> and is related to biclustering 
    frameworks as reviewed by Madeira and Oliveira (2004) <doi:10.1109/TCBB.2004.2>.

Provides an implementation of simplicial complexes for 
    Topological Data Analysis (TDA). The package includes functions to 
    compute faces, boundary operators, Betti numbers, Euler characteristic, 
    and to construct simplicial complexes. It also implements persistent 
    homology, from building filtrations to computing persistence diagrams, 
    with the aim of helping readers understand the core concepts of 
    computational topology.
    Methods are based on standard references in persistent homology such as 
    Zomorodian and Carlsson (2005) <doi:10.1007/s00454-004-1146-y> and 
    Chazal and Michel (2021) <doi:10.3389/frai.2021.667963>.