{
  "_id": "6ac65cab3d63260e0fa68e7f",
  "Package": "ClusterR",
  "Type": "Package",
  "Title": "Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids\nand Affinity Propagation Clustering",
  "Version": "1.3.7",
  "Date": "2026-09-07",
  "Authors@R": "c( person(given = \"Lampros\", family = \"Mouselimis\", email = \"mouselimislampros@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-8024-1546\")), person(given = \"Conrad\", family = \"Sanderson\", role = \"cph\", comment = \"Author of the C++ Armadillo library\"), person(given = \"Ryan\", family = \"Curtin\", role = \"cph\", comment = \"Author of the C++ Armadillo library\"), person(given = \"Siddharth\", family = \"Agrawal\", role = \"cph\", comment = \"Author of the C code of the Mini-Batch-Kmeans algorithm (https://github.com/siddharth-agrawal/Mini-Batch-K-Means)\"), person(given = \"Brendan\", family = \"Frey\", email = \"frey@psi.toronto.edu\", role = \"cph\", comment = \"Author of the matlab code of the Affinity propagation algorithm (for commercial use please contact the author of the matlab code)\"), person(given = \"Delbert\", family = \"Dueck\", role = \"cph\", comment = \"Author of the matlab code of the Affinity propagation algorithm\"), person(given = \"Vitalie\", family = \"Spinu\", email = \"spinuvit@gmail.com\", role = \"ctb\", comment = \"Github Contributor\"),person(given = \"Frederiek - Maarten\", family = \"Kerckhof\", email = \"fm@kytos.be\", role = \"ctb\", comment = \"Github Contributor\") )",
  "BugReports": "https://github.com/mlampros/ClusterR/issues",
  "URL": "https://github.com/mlampros/ClusterR,\nhttps://mlampros.github.io/ClusterR/",
  "Description": "Gaussian mixture models, k-means, mini-batch-kmeans,\nk-medoids and affinity propagation clustering with the option\nto plot, validate, predict (new data) and estimate the optimal\nnumber of clusters. The package takes advantage of\n'RcppArmadillo' to speed up the computationally intensive parts\nof the functions. For more information, see (i) \"Clustering in\nan Object-Oriented Environment\" by Anja Struyf, Mia Hubert,\nPeter Rousseeuw (1997), Journal of Statistical Software,\n<doi:10.18637/jss.v001.i04>; (ii) \"Web-scale k-means\nclustering\" by D. Sculley (2010), ACM Digital Library,\n<doi:10.1145/1772690.1772862>; (iii) \"Armadillo: a\ntemplate-based C++ library for linear algebra\" by Sanderson et\nal (2016), The Journal of Open Source Software,\n<doi:10.21105/joss.00026>; (iv) \"Clustering by Passing Messages\nBetween Data Points\" by Brendan J. Frey and Delbert Dueck,\nScience 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976,\n<doi:10.1126/science.1136800>.",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "LazyData": "TRUE",
  "VignetteBuilder": "knitr",
  "RoxygenNote": "7.3.3",
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  "Repository": "https://mlampros.r-universe.dev",
  "Date/Publication": "2026-09-07 06:03:00 UTC",
  "RemoteUrl": "https://github.com/mlampros/clusterr",
  "RemoteRef": "HEAD",
  "RemoteSha": "280dfbb092db62dc54a8e4ca960316ac66e861a6",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-10-07 13:07:51 UTC",
    "User": "root"
  },
  "Author": "Lampros Mouselimis [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8024-1546>),\nConrad Sanderson [cph] (Author of the C++ Armadillo library),\nRyan Curtin [cph] (Author of the C++ Armadillo library),\nSiddharth Agrawal [cph] (Author of the C code of the Mini-Batch-Kmeans\nalgorithm\n(https://github.com/siddharth-agrawal/Mini-Batch-K-Means)),\nBrendan Frey [cph] (Author of the matlab code of the Affinity\npropagation algorithm (for commercial use please contact the author\nof the matlab code)),\nDelbert Dueck [cph] (Author of the matlab code of the Affinity\npropagation algorithm),\nVitalie Spinu [ctb] (Github Contributor),\nFrederiek - Maarten Kerckhof [ctb] (Github Contributor)",
  "Maintainer": "Lampros Mouselimis <mouselimislampros@gmail.com>",
  "_user": "mlampros",
  "_type": "src",
  "_file": "ClusterR_1.3.7.tar.gz",
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  "_expires": "2027-01-15T14:52:25.000Z",
  "_created": "2026-10-07T13:07:51.000Z",
  "_published": "2026-10-07T14:52:27.359Z",
  "_buildurl": "https://github.com/r-universe/mlampros/actions/runs/37625520750",
  "_status": "success",
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  "_exports": [
    "AP_affinity_propagation",
    "AP_preferenceRange",
    "center_scale",
    "Clara_Medoids",
    "Cluster_Medoids",
    "cost_clusters_from_dissim_medoids",
    "distance_matrix",
    "external_validation",
    "GMM",
    "KMeans_arma",
    "KMeans_rcpp",
    "MiniBatchKmeans",
    "Optimal_Clusters_GMM",
    "Optimal_Clusters_KMeans",
    "Optimal_Clusters_Medoids",
    "plot_2d",
    "predict_GMM",
    "predict_KMeans",
    "predict_MBatchKMeans",
    "predict_Medoids",
    "Silhouette_Dissimilarity_Plot",
    "silhouette_of_clusters"
  ],
  "_datasets": [
    {
      "name": "dietary_survey_IBS",
      "title": "Synthetic data using a dietary survey of patients with irritable bowel syndrome (IBS)",
      "object": "dietary_survey_IBS",
      "class": [
        "data.frame"
      ],
      "fields": [
        "bread",
        "wheat",
        "pasta",
        "breakfast_cereal",
        "yeast",
        "spicy_food",
        "curry",
        "chinese_takeaway",
        "chilli",
        "cabbage",
        "onion",
        "garlic",
        "potatoes",
        "pepper",
        "vegetables_unspecified",
        "tomato",
        "beans_and_pulses",
        "mushroom",
        "fatty_foods_unspecified",
        "sauces",
        "chocolate",
        "fries",
        "crisps",
        "desserts",
        "eggs",
        "red_meat",
        "processed_meat",
        "pork",
        "chicken",
        "fish_shellfish",
        "dairy_products_unspecified",
        "cheese",
        "cream",
        "milk",
        "fruit_unspecified",
        "nuts_and_seeds",
        "orange",
        "apple",
        "banana",
        "grapes",
        "alcohol",
        "caffeine",
        "class"
      ],
      "rows": 400,
      "table": true,
      "tojson": true
    },
    {
      "name": "mushroom",
      "title": "The mushroom data",
      "object": "mushroom",
      "class": [
        "data.frame"
      ],
      "fields": [
        "class",
        "cap_shape",
        "cap_surface",
        "cap_color",
        "bruises",
        "odor",
        "gill_attachment",
        "gill_spacing",
        "gill_size",
        "gill_color",
        "stalk_shape",
        "stalk_root",
        "stalk_surface_above_ring",
        "stalk_surface_below_ring",
        "stalk_color_above_ring",
        "stalk_color_below_ring",
        "veil_type",
        "veil_color",
        "ring_number",
        "ring_type",
        "spore_print_color",
        "population",
        "habitat"
      ],
      "rows": 8124,
      "table": true,
      "tojson": true
    },
    {
      "name": "soybean",
      "title": "The soybean (large) data set from the UCI repository",
      "object": "soybean",
      "class": [
        "data.frame"
      ],
      "fields": [
        "date",
        "plant_stand",
        "precip",
        "temp",
        "hail",
        "crop_hist",
        "area_damaged",
        "severity",
        "seed_tmt",
        "germination",
        "plant_growth",
        "leaves",
        "leafspots_halo",
        "leafspots_marg",
        "leafspot_size",
        "leaf_shread",
        "leaf_malf",
        "leaf_mild",
        "stem",
        "lodging",
        "stem_cankers",
        "canker_lesion",
        "fruiting_bodies",
        "external_decay",
        "mycelium",
        "int_discolor",
        "sclerotia",
        "fruit_pods",
        "fruit_spots",
        "seed",
        "mold_growth",
        "seed_discolor",
        "seed_size",
        "shriveling",
        "roots",
        "class"
      ],
      "rows": 307,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "AP_affinity_propagation",
      "title": "Affinity propagation clustering",
      "topics": [
        "AP_affinity_propagation"
      ]
    },
    {
      "page": "AP_preferenceRange",
      "title": "Affinity propagation preference range",
      "topics": [
        "AP_preferenceRange"
      ]
    },
    {
      "page": "center_scale",
      "title": "Function to scale and/or center the data",
      "topics": [
        "center_scale"
      ]
    },
    {
      "page": "Clara_Medoids",
      "title": "Clustering large applications",
      "topics": [
        "Clara_Medoids"
      ]
    },
    {
      "page": "Cluster_Medoids",
      "title": "Partitioning around medoids",
      "topics": [
        "Cluster_Medoids"
      ]
    },
    {
      "page": "cost_clusters_from_dissim_medoids",
      "title": "Compute the cost and clusters based on an input dissimilarity matrix and medoids",
      "topics": [
        "cost_clusters_from_dissim_medoids"
      ]
    },
    {
      "page": "dietary_survey_IBS",
      "title": "Synthetic data using a dietary survey of patients with irritable bowel syndrome (IBS)",
      "topics": [
        "dietary_survey_IBS"
      ]
    },
    {
      "page": "distance_matrix",
      "title": "Distance matrix calculation",
      "topics": [
        "distance_matrix"
      ]
    },
    {
      "page": "external_validation",
      "title": "external clustering validation",
      "topics": [
        "external_validation"
      ]
    },
    {
      "page": "GMM",
      "title": "Gaussian Mixture Model clustering",
      "topics": [
        "GMM"
      ]
    },
    {
      "page": "KMeans_arma",
      "title": "k-means using the Armadillo library",
      "topics": [
        "KMeans_arma"
      ]
    },
    {
      "page": "KMeans_rcpp",
      "title": "k-means using RcppArmadillo",
      "topics": [
        "KMeans_rcpp"
      ]
    },
    {
      "page": "MiniBatchKmeans",
      "title": "Mini-batch-k-means using RcppArmadillo",
      "topics": [
        "MiniBatchKmeans"
      ]
    },
    {
      "page": "mushroom",
      "title": "The mushroom data",
      "topics": [
        "mushroom"
      ]
    },
    {
      "page": "Optimal_Clusters_GMM",
      "title": "Optimal number of Clusters for the gaussian mixture models",
      "topics": [
        "Optimal_Clusters_GMM"
      ]
    },
    {
      "page": "Optimal_Clusters_KMeans",
      "title": "Optimal number of Clusters for Kmeans or Mini-Batch-Kmeans",
      "topics": [
        "Optimal_Clusters_KMeans"
      ]
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