Package: ClustBlock 4.0.0

Fabien Llobell

ClustBlock: Clustering of Datasets

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. 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, RATA, Free Sorting and JAR experiments are available. Clustering of rows in multi-block context (notably with ClusMB strategy) is also included.

Authors:Fabien Llobell [aut, cre], Evelyne Vigneau [ctb], Veronique Cariou [ctb], El Mostafa Qannari [ctb]

ClustBlock_4.0.0.tar.gz
ClustBlock_4.0.0.tar.gz(r-4.5-noble)ClustBlock_4.0.0.tar.gz(r-4.4-noble)
ClustBlock_4.0.0.tgz(r-4.4-emscripten)ClustBlock_4.0.0.tgz(r-4.3-emscripten)
ClustBlock.pdf |ClustBlock.html
ClustBlock/json (API)
NEWS

# Install 'ClustBlock' in R:
install.packages('ClustBlock', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • RATAchoc - RATA data on chocolates
  • cheese - Cheese Just About Right data
  • choc - Chocolates data
  • fish - Fish data
  • smoo - Smoothies data
  • straw - Strawberries data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.36 score 23 scripts 371 downloads 24 exports 99 dependencies

Last updated 6 months agofrom:de815ab879. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-linuxOKNov 19 2024

Exports:catatiscatatis_jarcatatis_ratachange_cata_formatchange_cata_format2cluscatacluscata_jarcluscata_kmeanscluscata_kmeans_jarcluscata_rataClusMBclustatisclustatis_FreeSortclustatis_FreeSort_kmeansclustatis_kmeansclustRowsOnStatisAxesconsistency_cataconsistency_cata_panelindicesClusterspreprocess_FreeSortpreprocess_JARsimil_groups_catastatisstatis_FreeSort

Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacliclustercolorspacecowplotcpp11crosstalkDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluateFactoMineRfansifarverfastmapflashClustfontawesomeFormulafsgenericsggplot2ggrepelgluegtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownsassscalesscatterplot3dSparseMstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Clustering of DatasetsClustBlock-package ClustBlock
Perform the CATATIS method on different blocks from a CATA experimentcatatis
Perform the CATATIS method on Just About Right data.catatis_jar
Perform the CATATIS method on different blocks from a RATA experimentcatatis_rata
Change format of CATA datasets to perform CATATIS or CLUSCATA functionchange_cata_format
Change format of CATA datasets to perform the package functionschange_cata_format2
cheese Just About Right datacheese
chocolates datachoc
Perform a cluster analysis of subjects from a CATA experimentcluscata
Perform a cluster analysis of subjects in a JAR experiment.cluscata_jar
Compute the CLUSCATA partitioning algorithm on different blocks from a CATA experiment. Can be performed using a multi-start strategy or initial partition provided by the user.cluscata_kmeans
Perform a cluster analysis of subjects in a JAR experiment.cluscata_kmeans_jar
Perform a cluster analysis of subjects from a RATA experimentcluscata_rata
Perform a cluster analysis of rows in a Multi-block context with the ClusMB methodClusMB
Perform a cluster analysis of blocks of quantitative variablesclustatis
Perform a cluster analysis of free sorting dataclustatis_FreeSort
Compute the CLUSTATIS partitionning algorithm on free sorting dataclustatis_FreeSort_kmeans
Compute the CLUSTATIS partitioning algorithm on different blocks of quantitative variables. Can be performed using a multi-start strategy or initial partition provided by the user.clustatis_kmeans
Perform a cluster analysis of rows in a Multi-block context with clustering on STATIS axesclustRowsOnStatisAxes
Test the consistency of each attribute in a CATA experimentconsistency_cata
Test the consistency of the panel in a CATA experimentconsistency_cata_panel
fish datafish
Compute the indices to evaluate the quality of the cluster partition in multi-block contextindicesClusters
Displays the CATATIS graphsplot.catatis
Displays the CLUSCATA graphsplot.cluscata
Displays the CLUSTATIS graphsplot.clustatis
Displays the STATIS graphsplot.statis
Preprocessing for Free Sorting Datapreprocess_FreeSort
Preprocessing for Just About Right Datapreprocess_JAR
Print the CATATIS resultsprint.catatis
Print the CLUSCATA resultsprint.cluscata
Print the ClusMB or clustering on STATIS axes resultsprint.clusRows
Print the CLUSTATIS resultsprint.clustatis
Print the STATIS resultsprint.statis
RATA data on chocolatesRATAchoc
Testing the difference in perception between two predetermined groups of subjects in a CATA experimentsimil_groups_cata
smoothies datasmoo
Performs the STATIS method on different blocks of quantitative variablesstatis
Performs the STATIS method on Free Sorting datastatis_FreeSort
strawberries datastraw
Show the CATATIS resultssummary.catatis
Show the CLUSCATA resultssummary.cluscata
Show the ClusMB or clustering on STATIS axes resultssummary.clusRows
Show the CLUSTATIS resultssummary.clustatis
Show the STATIS resultssummary.statis