ClustBlock-package {ClustBlock}

Clustering of Datasets

Description

Hierarchical and partitioning algorithms of blocks of variables.The CLUSTATIS method and the CLUSCATA method 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, RATA, Free Sorting and JAR experiments are available.

Details

Package:	ClustBlock
Type:	Package
Version:	3.2.0
First version Date:	2019-03-06
Last version Date:	2023-08-30

Author(s)

Fabien Llobell, Evelyne Vigneau, Veronique Cariou, El Mostafa Qannari

Maintainer: [e-mail]

References

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2020). Analysis and clustering of multiblock datasets

by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food

Quality and Preference, 79, 103520.

Llobell, F., Vigneau, E., & Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification

of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data

and the clustering of subjects in a CATA experiment. Food quality and preference, 72, 31-39.

Llobell, F., Giacalone, D., Labenne, A., & Qannari, E. M. (2019). Assessment of the agreement and cluster analysis of

the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.

Llobell, F., & Qannari, E. M. (2020). CLUSTATIS: Cluster analysis of blocks of variables. Electronic

Journal of Applied Statistical Analysis, 13(2), 436-453.

Llobell, F. (2020). Classification de tableaux de données, applications en analyse sensorielle (Doctoral

dissertation, Nantes, Ecole nationale vétérinaire).