clusterMI: Cluster Analysis with Missing Values by Multiple Imputation

Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 <doi:10.1007/s11634-022-00519-1>. 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, as discussed in Audigier et al. (2021) <doi:10.48550/arXiv.2106.04424>. 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 (see Fang, Y. and Wang, J., 2012 <doi:10.1016/j.csda.2011.09.003>). 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.

Version: 1.2.1
Depends: R (≥ 3.5.0)
Imports: stats, graphics, parallel, mice, micemd, mclust, mix, fpc, knockoff, withr, glmnet, ClusterR, FactoMineR, diceR, NPBayesImputeCat, e1071, Rfast, cat, utils, ggplot2, gridExtra, reshape2, methods, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, stargazer, VIM, missMDA, clustrd, clusterCrit, bookdown
Published: 2024-07-07
DOI: 10.32614/CRAN.package.clusterMI
Author: Vincent Audigier [aut, cre] (CNAM MSDMA team), Hang Joon Kim [ctb] (University of Cincinnati)
Maintainer: Vincent Audigier <vincent.audigier at>
License: GPL-2 | GPL-3
NeedsCompilation: yes
Citation: clusterMI citation info
In views: Cluster
CRAN checks: clusterMI results


Reference manual: clusterMI.pdf
Vignettes: clusterMI: Cluster Analysis with Missing Values by Multiple Imputation


Package source: clusterMI_1.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): clusterMI_1.2.1.tgz, r-oldrel (arm64): clusterMI_1.2.1.tgz, r-release (x86_64): clusterMI_1.2.1.tgz, r-oldrel (x86_64): clusterMI_1.2.1.tgz
Old sources: clusterMI archive


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