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ClustBlock (version 4.1.0)

Clustering of Datasets

Description

Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) , Llobell, Vigneau & Qannari (2019) ) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) , Llobell, Giacalone, Labenne & Qannari (2019) ) 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. Clustering of rows in multi-block context (notably with ClusMB strategy) is also included.

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Version

Install

install.packages('ClustBlock')

Monthly Downloads

327

Version

4.1.0

License

MIT + file LICENSE

Maintainer

Fabien Llobell

Last Published

May 13th, 2025

Functions in ClustBlock (4.1.0)

choc

chocolates data
catatis

Perform the CATATIS method on different blocks from a CATA experiment
cluscata

Perform a cluster analysis of subjects from a CATA experiment
catatis_rata

Perform the CATATIS method on different blocks from a RATA experiment
plot.statis

Display the STATIS charts
print.clustatis

Print the CLUSTATIS results
summary.cluscata

Show the CLUSCATA results
print.cluscata

Print the CLUSCATA results
fish

fish data
print.clusRows

Print the ClusMB or clustering on STATIS axes results
summary.clusRows

Show the ClusMB or clustering on STATIS axes results
preprocess_FreeSort

Preprocessing for Free Sorting Data
print.statis

Print the STATIS results
clustatis

Perform a cluster analysis of blocks of quantitative variables
cluscata_jar

Perform a cluster analysis of subjects in a JAR experiment.
indicesClusters

Compute the indices to evaluate the quality of the cluster partition in multi-block context
clustatis_FreeSort

Perform a cluster analysis of free sorting data
straw

strawberries data
simil_groups_cata

Testing the difference in perception between two predetermined groups of subjects in a CATA experiment
ClustBlock-package

Clustering of Datasets
summary.catatis

Show the CATATIS results
consistency_cata_panel

Test the consistency of the panel in a CATA experiment
summary.clustatis

Show the CLUSTATIS results
clustatis_FreeSort_kmeans

Compute the CLUSTATIS partitioning algorithm on free sorting data
clustatis_kmeans

Compute the CLUSTATIS partitioning algorithm on different blocks of quantitative variables
plot.clusRows

Displays the ClusMB and clustRowsOnstatisAxes graphs
plot.catatis

Displays the CATATIS graphs
consistency_cata

Test the consistency of each attribute in a CATA experiment
ClusMB

Perform a cluster analysis of rows in a Multi-block context with the ClusMB method
preprocess_JAR

Preprocessing for Just About Right Data
cluscata_kmeans

Compute the CLUSCATA partitioning algorithm on different blocks from a CATA experiment
plot.cluscata

Displays the CLUSCATA graphs
plot.clustatis

Displays the CLUSTATIS graphs
cluscata_rata

Perform a cluster analysis of subjects from a RATA experiment
smoo

smoothies data
clustRowsOnStatisAxes

Perform a cluster analysis of rows in a Multi-block context with clustering on STATIS axes
summary.statis

Show the STATIS results
statis

Performs the STATIS method on different blocks of quantitative variables
cluscata_kmeans_jar

Perform a cluster analysis of subjects in a JAR experiment
print.catatis

Print the CATATIS results
statis_FreeSort

Performs the STATIS method on Free Sorting data
change_cata_format

Change format of CATA datasets to perform CATATIS or CLUSCATA function
catatis_jar

Perform the CATATIS method on Just About Right data.
RATAchoc

RATA data on chocolates
change_cata_format2

Change format of CATA datasets to perform the package functions
cheese

cheese Just About Right data