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 (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package.
| Version: |
2.4.1 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
FactoMineR |
| Suggests: |
ClustVarLV |
| Published: |
2022-03-31 |
| Author: |
Fabien Llobell [aut, cre] (Oniris/XLSTAT),
Evelyne Vigneau [ctb] (Oniris),
Veronique Cariou [ctb] (Oniris),
El Mostafa Qannari [ctb] (Oniris) |
| Maintainer: |
Fabien Llobell <fllobell at hotmail.fr> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
ClustBlock citation info |
| Materials: |
NEWS |
| CRAN checks: |
ClustBlock results |
Documentation:
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