Type: Package
Package: anticlust
Title: Subset Partitioning via Anticlustering
Version: 0.8.5
Authors@R: c(
    person("Martin", "Papenberg", , "martin.papenberg@hhu.de", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-9900-4268")),
    person("Meik", "Michalke", role = "ctb",
           comment = "centroid based clustering algorithm"),
    person(c("Gunnar", "W."), "Klau", role = "ths"),
    person(c("Juliane", "V."), "Nagel", role = "ctb",
           comment = "package logo"),
    person("Martin", "Breuer", role = "ctb",
           comment = "Bicriterion algorithm by Brusco et al."),
    person("Marie L.", "Schaper", role = "ctb",
           comment = "Example data set"),
    person("Max", "Diekhoff", role = "ctb",
           comment = "Optimal maximum dispersion algorithm")
  )
Author: Martin Papenberg [aut, cre] (<https://orcid.org/0000-0002-9900-4268>),
  Meik Michalke [ctb] (centroid based clustering algorithm),
  Gunnar W. Klau [ths],
  Juliane V. Nagel [ctb] (package logo),
  Martin Breuer [ctb] (Bicriterion algorithm by Brusco et al.),
  Marie L. Schaper [ctb] (Example data set),
  Max Diekhoff [ctb] (Optimal maximum dispersion algorithm)
Maintainer: Martin Papenberg <martin.papenberg@hhu.de>
Description: 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.
License: MIT + file LICENSE
URL: https://github.com/m-Py/anticlust,
        https://m-py.github.io/anticlust/
BugReports: https://github.com/m-Py/anticlust/issues
Depends: R (>= 3.6.0)
Imports: Matrix, RANN (>= 2.6.0)
Suggests: knitr, palmerpenguins, Rglpk, rmarkdown, Rsymphony, tinytest
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
RoxygenNote: 7.3.1
SystemRequirements: The exact (anti)clustering algorithms require that
        either the GNU linear programming kit (GLPK library) is
        installed (<http://www.gnu.org/software/glpk/>) or the SYMPHONY
        open source MILP solver
        (<https://github.com/coin-or/SYMPHONY>). Rendering the vignette
        requires pandoc (<https://pandoc.org/>).
Packaged: 2024-05-03 09:42:56 UTC; martin
Repository: CRAN
Date/Publication: 2024-05-05 21:20:15 UTC
Built: R 4.2.3; aarch64-apple-darwin20; 2024-05-05 23:19:37 UTC; unix
Archs: anticlust.so.dSYM
