R packages - Mar 2024 - clusterMI: Cluster Analysis with Missing Values by Multiple Imputation

Dear all,

I am pleased to announce the release of a new package named 'clusterMI'
on CRAN.

clusterMI allows clustering of incomplete observations by addressing missing
values using multiple imputation.

For achieving this goal, the methodology consists in three steps:

1. missing data imputation using tailored imputation models: four multiple
imputation methods are proposed, two are based on joint modelling (JM-GL and
JM-DP) and two are fully sequential methods (FCS-homo and FCS-hetero).
2. cluster analysis of imputed data sets: six clustering methods are available
(kmeans, pam, clara, hierarchical clustering, fuzzy c-means and gaussian
mixture), but custom methods can also be easily used.
3. partition pooling: the set of partitions is aggregated using NMF 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 offers several diagnostic tools for tuning the number of
imputed data sets, for checking convergence in sequential imputation, for
checking the fit of imputation models, etc.

This is the first version of the package, your feedback and suggestions are
welcome!

Please find more details and download the package from the following
link:https://cran.r-project.org/package=clusterMI

Best regards,

V. Audigier

-- 
Vincent AUDIGIER
Associate Professor, CNAM
2 rue Cont? 75003 Paris
Office 35.3.21
Tel: 01 40 27 27 19
Website:http://vincentaudigier.weebly.com/