Encoding: UTF-8
Package: clusterMI
Type: Package
Title: Cluster Analysis with Missing Values by Multiple Imputation
Version: 1.0.0
Date: 2024-03-11
Authors@R: c(
  person("Vincent","Audigier",email="vincent.audigier@cnam.fr",role=c("aut","cre"),comment="CNAM MSDMA team"),
  person("Hang Joon","Kim",email="hang.kim@uc.edu",role=c("ctb"),comment="University of Cincinnati"))
Description: Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps. 
    I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods.
    II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used.
    III) Partition pooling, The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap. Among applications, this instability measure can be used to choose a number of clusters with missing values.
    The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc.
License: GPL-2 | GPL-3
Depends: R (>= 3.5.0)
Imports: stats, graphics, parallel, mice, micemd, mclust, mix, fpc,
        usedist, knockoff, withr, glmnet, cluster, ClusterR,
        FactoMineR, diceR, NPBayesImputeCat, e1071, Rfast, cat, utils,
        lattice, reshape2, methods, Rcpp
RoxygenNote: 7.3.1
Suggests: knitr, rmarkdown, stargazer, VIM, missMDA, clustrd,
        clusterCrit, ggplot2, bookdown
VignetteBuilder: knitr
LazyData: true
LinkingTo: Rcpp, RcppArmadillo
RcppModules: IO_module
NeedsCompilation: yes
Packaged: 2024-03-12 07:08:54 UTC; vince
Author: Vincent Audigier [aut, cre] (CNAM MSDMA team),
  Hang Joon Kim [ctb] (University of Cincinnati)
Maintainer: Vincent Audigier <vincent.audigier@cnam.fr>
Repository: CRAN
Date/Publication: 2024-03-12 20:10:05 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 03:52:41 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
