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
#' @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 modeled by a mixture of ISR, whereas conditional independence
#' assumption is considered for multivariate rankings.
#'
#' @details
#' The main function is \link{rankclust}.
#' See vignettes for detailed examples: \code{RShowDoc("dataFormat", package = "Rankcluster")} and
#' \code{RShowDoc("Rankcluster", package = "Rankcluster")}
#'
#'
#' @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.
#'
#'
#' @examples
#' # see vignettes
#' # RShowDoc("dataFormat", package = "Rankcluster")
#' # RShowDoc("Rankcluster", package = "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)
#'
#' if(result@@convergence) {
#'   summary(result)
#'
#'   partition <- result[2]@@partition
#'   tik <- result[2]@@tik
#' }
#'
#' @keywords package
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