Package: varclust 0.10.0

varclust: Variables Clustering

Performs clustering of quantitative variables, assuming that clusters lie in low-dimensional subspaces. Segmentation of variables, number of clusters and their dimensions are selected based on BIC. Candidate models are identified based on many runs of K-means algorithm with different random initializations of cluster centers.

Authors:Piotr Sobczyk, Stanislaw Wilczynski, Julie Josse, Malgorzata Bogdan

varclust_0.10.0.tar.gz
varclust_0.10.0.zip(r-4.7)varclust_0.10.0.zip(r-4.6)varclust_0.10.0.zip(r-4.5)
varclust_0.10.0.tgz(r-4.6-any)varclust_0.10.0.tgz(r-4.5-any)
varclust_0.10.0.tar.gz(r-4.7-any)varclust_0.10.0.tar.gz(r-4.6-any)
varclust_0.10.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
varclust/json (API)

# Install 'varclust' in R:
install.packages('varclust', repos = c('https://psobczyk.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/psobczyk/varclust/issues

On CRAN:

Conda:

4.32 score 3 stars 14 scripts 150 downloads 1 mentions 8 exports 12 dependencies

Last updated from:c2b23b96a1. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR304
source / vignettesOK262
linux-release-x86_64ERROR272
macos-release-arm64ERROR197
macos-oldrel-arm64ERROR184
windows-develERROR242
windows-releaseERROR262
windows-oldrelERROR318
wasm-releaseOK100

Exports:data.simulationdata.simulation.factorsintegrationmisclassificationmlcc.bicmlcc.kmeansmlcc.repsshow.clusters

Dependencies:codetoolsdigestdoParalleldoRNGforeachiteratorslatticeMatrixpeselRcppRcppEigenrngtools

varclust package tutorial

Piotr Sobczyk

Rendered fromvarclustTutorial.Rmdusingknitr::rmarkdownon May 27 2026.

Last update: 2020-03-08
Started: 2014-11-01