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varclust
View on CRAN: Click
here
Download and install varclust package within the R console
Install from CRAN:
install.packages("varclust")
Install from Github:
library("remotes")
install_github("cran/varclust")
Install by package version:
library("remotes")
install_version("varclust", "0.9.4")
Attach the package and use:
library("varclust")
Maintained by
Piotr Sobczyk
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.varclust . varclust results . varclust.pdf . varclust tutorial . varclust_0.9.4.tar.gz . varclust_0.9.4.zip . varclust_0.9.4.zip . varclust_0.9.4.zip . varclust_0.9.4.tgz . varclust_0.9.4.tgz . varclust_0.9.4.tgz . varclust_0.9.4.tgz . varclust_0.9.4.tgz . varclust_0.9.4.tgz . https://CRAN.R-project.org/package=varclust .
First Published: 2019-06-26
Latest Update: 2019-06-26
Description:
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.
How to cite:
Piotr Sobczyk (2019). varclust: Variables Clustering. R package version 0.9.4, https://cran.r-project.org/web/packages/varclust. Accessed 06 Apr. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited varclust R package
View varclust citation profile
Other R packages that varclust depends,
imports, suggests or enhances
Complete documentation for varclust
Functions, R codes and Examples using
the varclust R package
Some associated functions: calculate.pcas . choose.cluster.BIC . cluster.pca.BIC . data.simulation.factors . data.simulation . integration . misclassification . mlcc.bic . mlcc.kmeans . mlcc.reps . plot.mlcc.fit . print.mlcc.fit . print.mlcc.reps.fit . show.clusters . varclust .
Some associated R codes: auxiliary.functions.R . data.simulation.R . integration.R . misclassification.R . mlcc.bic.R . mlcc.kmeans.R . mlcc.reps.R . varclust.R . Full varclust package functions and examples
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