Type: Package
Package: anticlust
Title: Subset Partitioning via Anticlustering
Version: 0.6.3
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")
  )
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)
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 low similarity of
    the different 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 function
    anticlustering() implements exact and heuristic anticlustering
    algorithms as described in Papenberg and Klau (2021;
    <doi:10.1037/met0000301>). The exact algorithms require that the GNU
    linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>)
    is available and the R package 'Rglpk'
    (<https://cran.R-project.org/package=Rglpk>) is installed. A
    bicriterion anticlustering method proposed by Brusco et al.  (2020;
    <doi:10.1111/bmsp.12186>) is available through the function
    bicriterion_anticlustering(), kplus_anticlustering() implements the
    k-plus anticlustering approach proposed by Papenberg (2023;
    <doi:10.31234/osf.io/7jw6v>).  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
BugReports: https://github.com/m-Py/anticlust/issues
Depends: R (>= 3.6.0)
Imports: Matrix, RANN (>= 2.6.0)
Suggests: knitr, Rglpk, rmarkdown, testthat
VignetteBuilder: knitr, rmarkdown
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
RoxygenNote: 7.2.3
SystemRequirements: The exact (anti)clustering algorithms require that
        the GNU linear programming kit (GLPK library) is installed
        (<http://www.gnu.org/software/glpk/>). Rendering the vignette
        requires pandoc.
Packaged: 2023-01-30 12:48:45 UTC; martin
Repository: CRAN
Date/Publication: 2023-01-30 14:30:02 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 14:39:36 UTC; windows
Archs: i386, x64
