Package: Rankcluster
Type: Package
Title: Model-Based Clustering for Multivariate Partial Ranking Data
Version: 0.98.0
Date: 2022-11-11
Authors@R: c(person("Quentin", "Grimonprez", role = c("aut", "cre"), email = "quentingrim@yahoo.fr"),
             person("Julien", "Jacques", role = "aut", email = "julien.jacques@univ-lyon2.fr"),
             person("Christophe", "Biernacki", role = "aut", email = "christophe.biernacki@inria.fr"))
Description: Implementation of a model-based clustering algorithm for
    ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 
    Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
License: GPL (>= 2)
Copyright: Inria - Université de Lille
BugReports: https://github.com/modal-inria/Rankcluster/issues/
Depends: R (>= 2.10)
Imports: Rcpp, methods
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.2.1
NeedsCompilation: yes
Packaged: 2022-11-11 19:24:51 UTC; quentin
Author: Quentin Grimonprez [aut, cre],
  Julien Jacques [aut],
  Christophe Biernacki [aut]
Maintainer: Quentin Grimonprez <quentingrim@yahoo.fr>
Repository: CRAN
Date/Publication: 2022-11-12 01:20:08 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 15:50:23 UTC; windows
Archs: i386, x64
