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README

What is this repository for?

Package varclust

  • is R package for clustering quantitative variables
  • provides estimation of number of clusters
  • enables significant data dimension reduction

Check out demo version!

How do I get set up?

  • Install varclust package using devtools package
install_github("psobczyk/varclust")
  • Download the package as an archive and install it manually from R console

  • You might need to install package dependencies:

    • parallel
    • doParallel
    • foreach
    • doRNG
    • RcppEigen
    • pesel
  • The pesel package can be installed using devtools package

install_github("psobczyk/pesel")
  • No additional configuration is needed
  • Read vignette to get familiar with basic usage

Who do I talk to?

  • If help provided in the package documentation does not solve your problem

please contact Piotr.Sobczyk[at]pwr.edu.pl


This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 602552.

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Version

Install

install.packages('varclust')

Monthly Downloads

140

Version

0.9.4

License

GPL-3

Maintainer

Piotr Sobczyk

Last Published

June 26th, 2019

Functions in varclust (0.9.4)

mlcc.reps

Multiple Latent Components Clustering - Subspace clustering assuming that the number of clusters is known
varclust

Variable Clustering with Multiple Latent Components Clustering algorithm
print.mlcc.fit

Print mlcc.fit class object
plot.mlcc.fit

Plot mlcc.fit class object
print.mlcc.reps.fit

Print mlcc.reps.fit class object
show.clusters

Print clusters obtained from MLCC
data.simulation.factors

Simulates subspace clustering data with shared factors
calculate.pcas

Calculates principal components for every cluster
choose.cluster.BIC

Choses a subspace for a variable
integration

Computes integration and acontamination of the clustering
cluster.pca.BIC

mBIC for subspace clustering
data.simulation

Simulates subspace clustering data
misclassification

Computes misclassification rate
mlcc.bic

Multiple Latent Components Clustering - Subspace clustering with automatic estimation of number of clusters and their dimension
mlcc.kmeans

Multiple Latent Components Clustering - kmeans algorithm