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clusterMI  

Cluster Analysis with Missing Values by Multiple Imputation
View on CRAN: Click here


Download and install clusterMI package within the R console
Install from CRAN:
install.packages("clusterMI")

Install from Github:
library("remotes")
install_github("cran/clusterMI")

Install by package version:
library("remotes")
install_version("clusterMI", "1.2.2")



Attach the package and use:
library("clusterMI")
Maintained by
Vincent Audigier
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-03-12
Latest Update: 2024-03-12
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.
How to cite:
Vincent Audigier (2024). clusterMI: Cluster Analysis with Missing Values by Multiple Imputation. R package version 1.2.2, https://cran.r-project.org/web/packages/clusterMI. Accessed 12 Nov. 2024.
Previous versions and publish date:
1.0.0 (2024-03-12 21:10), 1.1.0 (2024-05-17 16:50), 1.1.1 (2024-05-31 15:30), 1.2.1 (2024-07-07 18:00), 1.2 (2024-07-04 16:40)
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