Nothing
#' @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
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.