R/Rankcluster-package.R

In Rankcluster: Model-Based Clustering for Multivariate Partial Ranking Data

#' @import Rcpp methods
#' @useDynLib Rankcluster
#' 
#' @title Model-Based Clustering for Multivariate Partial Ranking Data
#' @docType package
#' @aliases Rankcluster-package, Rankcluster
#' @name Rankcluster-package
#' @description    This package proposes a model-based clustering algorithm for ranking data. 
#' 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.
#' 
#' @details
#' 
#' 
#' The main function is \link{rankclust}. 
#' 
#' @references   [1] C.Biernacki and J.Jacques (2013), A generative model for rank data based on sorting algorithm, Computational Statistics and Data Analysis, 58, 162-176.
#' 
#' [2] J.Jacques and C.Biernacki (2012), Model-based clustering for multivariate partial ranking data, Inria Research Report n 8113.
#' 
#' @author Maintainer: Quentin Grimonprez <quentin.grimonprez@@inria.fr>
#' 
#' @examples 
#' # see vignette
#' # vignette("Rankcluster")
#' 
#' # main function of the package for run the algorithm
#' data(big4)
#' result = rankclust(big4$data, K = 2, m = big4$m, Ql = 200, Bl = 100, maxTry = 2)
#'  
#' @keywords package
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