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vimpclust

Variable Importance in Clustering

v0.1.0 · Jan 8, 2021 · GPL-3

Description

An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.

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dependencies in R code

Namespaces in Imports field not imported from:
  ‘mclust’ ‘rlang’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespaces in Imports field not imported from:
  ‘mclust’ ‘rlang’
  All declared Imports should be used.

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-devel-linux-x86_64-fedora-clang

dependencies in R code

Namespaces in Imports field not imported from:
  ‘mclust’ ‘rlang’
  All declared Imports should be used.
NOTE r-devel-linux-x86_64-fedora-gcc

dependencies in R code

Namespaces in Imports field not imported from:
  ‘mclust’ ‘rlang’
  All declared Imports should be used.

Dependency Network

Dependencies Reverse dependencies PCAmixdata ggplot2 Polychrome mclust rlang vimpclust

Version History

new 0.1.0 Mar 10, 2026