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 optimal 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 optimal algorithms require that an
    integer linear programming solver is installed. This package will install
    'lpSolve' (<https://cran.r-project.org/package=lpSolve>)
    as a default solver, but it is also possible to 
    use the package 'Rglpk' (<https://cran.r-project.org/package=Rglpk>), 
    which requires the GNU linear programming 
    kit (<https://www.gnu.org/software/glpk/glpk.html>), or the 
    package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>), which
    requires the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>). 
    'Rglpk' and 'Rsymphony' have to be manually installed by the user because they 
    are only "suggested" dependencies. 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.