clusterMI

cranv1.6

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

License GPL-2 | GPL-30 versions1 maintainers24 deps108 weekly dl
https://CRAN.R-project.org/package=clusterMI
55
/ 100
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safe to use

[email protected] is safe to use (health: 55/100)

Health breakdown0 – 100
25/25
maintenance
3/20
popularity
25/25
security
0/15
maturity
2/15
community
Vulnerabilities
0
none known
⚠ Possible typosquat
Name is close to a popular package. Targets:
cluster (close_name dist 2)

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Dependencies (24)
statsgraphicsparallelmicemicemdmclustmixfpcknockoffwithrglmnetClusterRFactoMineRdiceRNPBayesImputeCate1071Rfastcatutilsggplot2gridExtrareshape2methodsRcpp
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First published · 2026-04-03 15:26:57

Last updated · 2026-04-03T12:50:02+00:00