ClustBlock: Clustering of Datasets

Hierarchical and partitioning algorithms of blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2018) <doi:10.1016/j.foodqual.2018.05.013>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>) are the core of this package.

Version: 2.0.0
Depends: R (≥ 3.4.0)
Imports: FactoMineR
Suggests: ClustVarLV
Published: 2019-07-08
Author: Fabien Llobell [aut, cre] (Oniris/XLSTAT), Evelyne Vigneau [ctb] (Oniris), Veronique Cariou [ctb] (Oniris), El Mostafa Qannari [ctb] (Oniris)
Maintainer: Fabien Llobell <fabien.llobell at oniris-nantes.fr>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: ClustBlock results

Downloads:

Reference manual: ClustBlock.pdf
Package source: ClustBlock_2.0.0.tar.gz
Windows binaries: r-devel: ClustBlock_2.0.0.zip, r-release: ClustBlock_2.0.0.zip, r-oldrel: ClustBlock_2.0.0.zip
OS X binaries: r-release: ClustBlock_2.0.0.tgz, r-oldrel: ClustBlock_2.0.0.tgz
Old sources: ClustBlock archive

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