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>.

Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.

Network sparsification with a variety of novel and known network sparsification 
    techniques. All network sparsification techniques reduce the number of edges, not the number 
    of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. 
    This package is based on the work of Spielman, D., Srivastava, N. (2009)<arXiv:0803.0929>.
    Koutis I., Levin, A., Peng, R. (2013)<arXiv:1209.5821>. Toivonen, H., Mahler, S., Zhou, F. 
    (2010)<doi:10.1007>. Foti, N., Hughes, J., Rockmore, D. (2011)<doi:10.1371>.

Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement.