Package: ClustBlock
Title: Clustering of Datasets
Version: 6.0.0
Authors@R: c(person("Fabien", "Llobell", email = "fabienllobellresearch@gmail.com", role = c("aut","cre"), comment="Oniris/XLSTAT"), person("Evelyne", "Vigneau", email = "evelyne.vigneau@oniris-nantes.fr", role = "ctb", comment="Oniris"),  person("Veronique", "Cariou", email = "veronique.cariou@oniris-nantes.fr", role = "ctb", comment="Oniris"), person("El Mostafa", "Qannari", email = "elmostafa.qannari@oniris-nantes.fr", role = "ctb", comment="Oniris"))
Maintainer: Fabien Llobell <fabienllobellresearch@gmail.com>
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) <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 (Llobell, Bonnet &
        Giacalone (2024) <doi:10.1111/joss.12941>) . Multivariate
        analysis and clustering of subjects for quantitative multiblock
        data, CATA, RATA, Free Sorting and JAR experiments are
        available. Clustering of observations (products in sensory
        analysis) in multi-block context (notably with ClusMB strategy)
        is also included (Llobell & Giacalone (2025)
        <doi:10.1111/joss.70024>).Performing clustering based on CATA
        and liking at the same time is possible thanks to
        cluscata_liking function (Vigneau, Cariou, Giacalone, Berget &
        Llobell (2022) <doi:10.1016/j.foodqual.2021.104358>).
        Clustering of variables (quantitative, qualitative or mixed)
        can be done thanks to the MixCluStatis() function.
Depends: R (>= 3.5)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: FactoMineR
Suggests: ClustVarLV
Date: 2026-06-04
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-07-18 04:41:06 UTC; root
Author: Fabien Llobell [aut, cre] (Oniris/XLSTAT), Evelyne Vigneau
        [ctb] (Oniris), Veronique Cariou [ctb] (Oniris), El Mostafa
        Qannari [ctb] (Oniris)
Config/pak/sysreqs: cmake libfreetype6-dev make libicu-dev libjpeg-dev
        libpng-dev libuv1-dev libxml2-dev libssl-dev zlib1g-dev
Repository: https://fabienllobellresearch.r-universe.dev
Date/Publication: 2026-06-04 17:00:07 UTC
RemoteUrl: https://github.com/cran/ClustBlock
RemoteRef: HEAD
RemoteSha: 6e770449ea3e2f83a72471c08d2ca60a4dba7708