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  "Title": "Clustering of Datasets",
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  "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\"))",
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  "Description": "Hierarchical and partitioning algorithms to cluster blocks\nof variables. The partitioning algorithm includes an option\ncalled noise cluster to set aside atypical blocks of variables.\nDifferent thresholds per cluster can be sets. The CLUSTATIS\nmethod (for quantitative blocks) (Llobell, Cariou, Vigneau,\nLabenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>,\nLlobell, Vigneau & Qannari (2019)\n<doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method\n(for Check-All-That-Apply data) (Llobell, Cariou, Vigneau,\nLabenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>,\nLlobell, Giacalone, Labenne & Qannari (2019)\n<doi:10.1016/j.foodqual.2019.05.017>) are the core of this\npackage. The CATATIS methods allows to compute some indices and\ntests to control the quality of CATA data (Llobell, Bonnet &\nGiacalone (2024) <doi:10.1111/joss.12941>) . Multivariate\nanalysis and clustering of subjects for quantitative multiblock\ndata, CATA, RATA, Free Sorting and JAR experiments are\navailable. Clustering of observations (products in sensory\nanalysis) in multi-block context (notably with ClusMB strategy)\nis also included (Llobell & Giacalone (2025)\n<doi:10.1111/joss.70024>).Performing clustering based on CATA\nand liking at the same time is possible thanks to\ncluscata_liking function (Vigneau, Cariou, Giacalone, Berget &\nLlobell (2022) <doi:10.1016/j.foodqual.2021.104358>).\nClustering of variables (quantitative, qualitative or mixed)\ncan be done thanks to the MixCluStatis() function.",
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      "page": "ClustBlock-package",
      "title": "Clustering of Datasets",
      "topics": [
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    {
      "page": "cata_ryebread",
      "title": "cata_ryebread data",
      "topics": [
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    {
      "page": "catatis",
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      "topics": [
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    {
      "page": "catatis_jar",
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      "topics": [
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    {
      "page": "catatis_rata",
      "title": "Perform the CATATIS method on different blocks from a RATA experiment",
      "topics": [
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    },
    {
      "page": "change_cata_format",
      "title": "Change format of CATA datasets to perform CATATIS or CLUSCATA function",
      "topics": [
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    {
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    {
      "page": "cluscata",
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      "topics": [
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    {
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      "topics": [
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    {
      "page": "cluscata_kmeans_jar",
      "title": "Perform a cluster analysis of subjects in a JAR experiment",
      "topics": [
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    },
    {
      "page": "cluscata_liking",
      "title": "Perform a cluster analysis of subjects on CATA/liking combination",
      "topics": [
        "cluscata_liking"
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    },
    {
      "page": "cluscata_rata",
      "title": "Perform a cluster analysis of subjects from a RATA experiment",
      "topics": [
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    },
    {
      "page": "ClusMB",
      "title": "Perform a cluster analysis of rows in a Multi-block context with the ClusMB method",
      "topics": [
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    },
    {
      "page": "clustatis",
      "title": "Perform a cluster analysis of blocks of quantitative variables",
      "topics": [
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    {
      "page": "clustatis_FreeSort",
      "title": "Perform a cluster analysis of free sorting data",
      "topics": [
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    },
    {
      "page": "clustatis_FreeSort_kmeans",
      "title": "Compute the CLUSTATIS partitioning algorithm on free sorting data",
      "topics": [
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    {
      "page": "clustatis_kmeans",
      "title": "Compute the CLUSTATIS partitioning algorithm on different blocks of quantitative variables",
      "topics": [
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      "page": "clustRowsOnStatisAxes",
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    {
      "page": "combinCATALiking",
      "title": "Combination of CATA and liking data for CLUSCATA-liking",
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      "page": "consistency_cata",
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      "title": "Compute the indices to evaluate the quality of the cluster partition in multi-block context",
      "topics": [
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    {
      "page": "liking_ryebread",
      "title": "liking_ryebread data",
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      "page": "mixclustatis",
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    {
      "page": "plot.cluscata_liking",
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    {
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      "topics": [
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    {
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      "page": "plot.statis",
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      "page": "preprocess_FreeSort",
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      "topics": [
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    {
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    {
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      "title": "Print the CATATIS results",
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      "page": "print.cluscata",
      "title": "Print the CLUSCATA results",
      "topics": [
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      "title": "Print the CLUSCATA-liking results",
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    {
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      "title": "Print the CLUSTATIS results",
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      "page": "print.statis",
      "title": "Print the STATIS results",
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      "title": "Testing the difference in perception between two predetermined groups of subjects in a CATA experiment",
      "topics": [
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    {
      "page": "smoo",
      "title": "smoothies data",
      "topics": [
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    {
      "page": "statis",
      "title": "Performs the STATIS method on different blocks of quantitative variables",
      "topics": [
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    {
      "page": "statis_FreeSort",
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    {
      "page": "straw",
      "title": "strawberries data",
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    {
      "page": "summary.catatis",
      "title": "Show the CATATIS results",
      "topics": [
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    {
      "page": "summary.cluscata",
      "title": "Show the CLUSCATA results",
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      "page": "summary.cluscata_liking",
      "title": "Show the CLUSCATA-liking results",
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    {
      "page": "summary.clusRows",
      "title": "Show the ClusMB or clustering on STATIS axes results",
      "topics": [
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    {
      "page": "summary.clustatis",
      "title": "Show the CLUSTATIS results",
      "topics": [
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    },
    {
      "page": "summary.statis",
      "title": "Show the STATIS results",
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