The R Journal: article published in 2015, volume 7:2

ClustVarLV: An R Package for the Clustering of Variables Around Latent Variables PDF download
Evelyne Vigneau, Mingkun Chen and El Mostafa Qannari , The R Journal (2015) 7:2, pages 134-148.

Abstract The clustering of variables is a strategy for deciphering the underlying structure of a data set. Adopting an exploratory data analysis point of view, the Clustering of Variables around Latent Variables (CLV) approach has been proposed by Vigneau and Qannari (2003). Based on a family of optimization criteria, the CLV approach is adaptable to many situations. In particular, constraints may be introduced in order to take account of additional information about the observations and/or the variables. In this paper, the CLV method is depicted and the R package ClustVarLV including a set of functions developed so far within this framework is introduced. Considering successively different types of situations, the underlying CLV criteria are detailed and the various functions of the package are illustrated using real case studies.

Received: 2015-03-04; online 2015-10-23
CRAN packages: cluster, ClustVarLV, ClustOfVar, clere, biclust, pvclust, Hmisc, FactoMineR, plsgenomics, Rcpp, ClustVarLV
CRAN Task Views implied by cited CRAN packages: Multivariate, Cluster, Psychometrics, Environmetrics, HighPerformanceComputing, Bayesian, ClinicalTrials, Econometrics, Graphics, NumericalMathematics, OfficialStatistics, ReproducibleResearch, SocialSciences

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This article is licensed under a Creative Commons Attribution 3.0 Unported license .

  author = {Evelyne Vigneau and Mingkun Chen and El Mostafa Qannari},
  title = {{ClustVarLV: An R Package for the Clustering of Variables
          Around Latent Variables}},
  year = {2015},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2015-026},
  url = {},
  pages = {134--148},
  volume = {7},
  number = {2}