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. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, Free Sorting and JAR experiments are available.
| Version: |
3.0.0 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
FactoMineR |
| Suggests: |
ClustVarLV |
| Published: |
2022-09-07 |
| 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 |
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