The method of anticlustering partitions a pool of elements
    into groups (i.e., anticlusters) with the goal of maximizing
    between-group similarity or within-group heterogeneity.  The
    anticlustering approach thereby reverses the logic of cluster analysis
    that strives for high within-group homogeneity and clear separation
    between groups.  Computationally, anticlustering is accomplished by
    maximizing instead of minimizing a clustering objective function, such
    as the intra-cluster variance (used in k-means clustering) or the sum
    of pairwise distances within clusters. The main function
    anticlustering() gives access to exact and heuristic anticlustering
    methods described in Papenberg and Klau (2021;
    <doi:10.1037/met0000301>), Brusco et al. (2020;
    <doi:10.1111/bmsp.12186>), and Papenberg (2024;
    <doi:10.1111/bmsp.12315>). The exact algorithms require that an
    integer linear programming solver is installed, either the GNU linear
    programming kit (<https://www.gnu.org/software/glpk/glpk.html>)
    together with the interface package 'Rglpk'
    (<https://cran.R-project.org/package=Rglpk>), or the SYMPHONY ILP
    solver (<https://github.com/coin-or/SYMPHONY>) together with the
    interface package 'Rsymphony'
    (<https://cran.r-project.org/package=Rsymphony>). Full access to the
    bicriterion anticlustering method proposed by Brusco et al. (2020) is
    given via the function bicriterion_anticlustering(), while
    kplus_anticlustering() implements the full functionality of the k-plus
    anticlustering approach proposed by Papenberg (2024). Some other
    functions are available to solve classical clustering problems. The
    function balanced_clustering() applies a cluster analysis under size
    constraints, i.e., creates equal-sized clusters. The function
    matching() can be used for (unrestricted, bipartite, or K-partite)
    matching. The function wce() can be used optimally solve the
    (weighted) cluster editing problem, also known as correlation
    clustering, clique partitioning problem or transitivity clustering.