R/miclust-package.R

In miclust: Multiple Imputation in Cluster Analysis

#' miclust-package: integrating multiple imputation with cluster analysis
#'
#' @description Cluster analysis with selection of the final number of clusters
#'   and an optional variable selection procedure. The package is designed to
#'   integrate the results of multiply imputed data sets while accounting for the
#'   uncertainty that the imputations introduce in the final results. See `Procedure'
#'   below for further details on how the tool works.
#' @section Procedure: The tool consists of a two-step procedure. In the first step,
#'   the user provides the data to be analyzed. They can be a single data.frame or a
#'   list of data.frames including the raw data and the imputed data sets. In the
#'   latter case, \code{getdata} needs to by used first to get data prepared. In the
#'   second step, the \code{miclust} performs k-means clustering with selection of
#'   the final number of clusters and an optional (backward or forward) variable
#'   selection procedure. Specific \code{summary} and \code{plot} methods are provided
#'   to summarize and visualize the impact of the imputations on the results.
#' @section Authors:
#'   Jose Barrera-Gomez (maintainer, <jose.barrera@isglobal.org>) and Xavier Basagana.
#' @references The methodology used in the package is described in
#'
#' Basagana X, Barrera-Gomez J, Benet M, Anto JM, Garcia-Aymerich J. A Framework
#' for Multiple Imputation in Cluster Analysis. \emph{American Journal of
#' Epidemiology}. 2013;177(7):718-725.
#' @docType package
#' @name miclust-package
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