{
  "_id": "6a1eda02b401979e7340f479",
  "Package": "rgeoda",
  "Type": "Package",
  "Title": "R Library for Spatial Data Analysis",
  "Version": "0.1.1",
  "Date": "2026-03-19",
  "Authors@R": "c(person(given = \"Xun\", family = \"Li\", email=\"lixun910@gmail.com\", role=c(\"aut\",\"cre\")),\nperson(given = \"Luc\", family = \"Anselin\", email=\"anselin@uchicago.edu\", role=\"aut\"))",
  "Maintainer": "Xun Li <lixun910@gmail.com>",
  "Description": "Provides spatial data analysis functionalities including\nExploratory Spatial Data Analysis, Spatial Cluster Detection\nand Clustering Analysis, Regionalization, etc. based on the C++\nsource code of 'GeoDa', which is an open-source software tool\nthat serves as an introduction to spatial data analysis. The\n'GeoDa' software and its documentation are available at\n<https://geodacenter.github.io>.",
  "URL": "https://github.com/geodacenter/rgeoda/,\nhttps://geodacenter.github.io/rgeoda/",
  "BugReports": "https://github.com/geodacenter/rgeoda/issues/",
  "License": "GPL (>= 2)",
  "Collate": "init.R rgeoda.R sf_geoda.R RcppExports.R read_geoda.R\nweights.R utils.R lisa.R clustering.R",
  "RoxygenNote": "7.3.2",
  "Encoding": "UTF-8",
  "SystemRequirements": "C++17",
  "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibssl-dev libproj-dev libsqlite3-dev libudunits2-dev",
  "Repository": "https://geodacenter.r-universe.dev",
  "Date/Publication": "2026-02-19 16:33:21 UTC",
  "RemoteUrl": "https://github.com/geodacenter/rgeoda",
  "RemoteRef": "HEAD",
  "RemoteSha": "92fcb9178febfbe0f8ea180c7f8d486b575e28e3",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-20 09:13:19 UTC",
    "User": "root"
  },
  "Author": "Xun Li [aut, cre],\nLuc Anselin [aut]",
  "MD5sum": "7e3ce9a4e16707846924b60184fc7fe9",
  "_user": "geodacenter",
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  "_created": "2026-05-20T09:13:19.000Z",
  "_published": "2026-06-02T13:26:26.389Z",
  "_distro": "noble",
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    "author": "Xun Li <lixun910@gmail.com>",
    "committer": "Xun Li <lixun910@gmail.com>",
    "message": "misc: update rhub.yaml\n",
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    {
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    {
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    {
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  "_tags": [],
  "_topics": [
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    "geoda",
    "geospatial",
    "cpp"
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  "_userbio": {
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    "type": "organization",
    "name": "GeoDa Center ",
    "description": "A joint initiative of the Division of Social Sciences and the Computation Institute, the Center for Spatial Data Science (CSDS) develops state-of-the-art method"
  },
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  "_devurl": "https://github.com/geodacenter/rgeoda",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/readme.html",
    "extra/readme.md",
    "extra/rgeoda.html",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/geodacenter/rgeoda",
  "_realowner": "geodacenter",
  "_cranurl": true,
  "_releases": [
    {
      "version": "0.0.6",
      "date": "2021-01-28"
    },
    {
      "version": "0.0.8",
      "date": "2021-02-26"
    },
    {
      "version": "0.0.8-1",
      "date": "2021-03-04"
    },
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      "version": "0.0.8-2",
      "date": "2021-07-19"
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    {
      "version": "0.1.0",
      "date": "2025-03-06"
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    {
      "version": "0.1.1",
      "date": "2026-02-20"
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  ],
  "_exports": [
    "as.geoda",
    "azp_greedy",
    "azp_sa",
    "azp_tabu",
    "create_weights",
    "distance_weights",
    "eb_rates",
    "eb_rates_standardization",
    "gda_distance_weights",
    "gda_kernel_knn_weights",
    "gda_kernel_weights",
    "gda_knn_weights",
    "gda_min_distthreshold",
    "gda_queen_weights",
    "gda_rook_weights",
    "geoda_open",
    "get_neighbors",
    "get_neighbors_weights",
    "has_isolates",
    "hinge15_breaks",
    "hinge30_breaks",
    "is_symmetric",
    "join_count_ratio",
    "kernel_knn_weights",
    "kernel_weights",
    "knn_weights",
    "lisa_bo",
    "lisa_clusters",
    "lisa_colors",
    "lisa_fdr",
    "lisa_labels",
    "lisa_num_nbrs",
    "lisa_pvalues",
    "lisa_values",
    "local_bijoincount",
    "local_bimoran",
    "local_g",
    "local_geary",
    "local_gstar",
    "local_joincount",
    "local_moran",
    "local_moran_eb",
    "local_multigeary",
    "local_multijoincount",
    "local_multiquantilelisa",
    "local_quantilelisa",
    "make_spatial",
    "max_neighbors",
    "maxp_greedy",
    "maxp_sa",
    "maxp_tabu",
    "mean_neighbors",
    "median_neighbors",
    "min_distthreshold",
    "min_neighbors",
    "natural_breaks",
    "neighbor_match_test",
    "p_GeoDa",
    "p_GeoDaTable",
    "p_GeoDaWeight",
    "p_LISA",
    "percentile_breaks",
    "quantile_breaks",
    "queen_weights",
    "read_gal",
    "read_gwt",
    "read_swm",
    "redcap",
    "rook_weights",
    "save_weights",
    "schc",
    "set_neighbors",
    "set_neighbors_with_weights",
    "sf_to_geoda",
    "skater",
    "sp_to_geoda",
    "spatial_lag",
    "spatial_validation",
    "stddev_breaks",
    "update_weights",
    "weights_sparsity"
  ],
  "_help": [
    {
      "page": "as.data.frame.geoda",
      "title": "convert rgeoda instance to data.frame",
      "topics": [
        "as.data.frame.geoda"
      ]
    },
    {
      "page": "as.geoda",
      "title": "Create an instance of geoda-class from either an 'sf' or 'sp' object",
      "topics": [
        "as.geoda"
      ]
    },
    {
      "page": "as.matrix.Weight",
      "title": "spatial weights to matrix",
      "topics": [
        "as.matrix.Weight"
      ]
    },
    {
      "page": "azp_greedy",
      "title": "A greedy algorithm to solve the AZP problem",
      "topics": [
        "azp_greedy"
      ]
    },
    {
      "page": "azp_sa",
      "title": "A simulated annealing algorithm to solve the AZP problem",
      "topics": [
        "azp_sa"
      ]
    },
    {
      "page": "azp_tabu",
      "title": "A tabu algorithm to solve the AZP problem",
      "topics": [
        "azp_tabu"
      ]
    },
    {
      "page": "create_weights",
      "title": "Create an empty weights",
      "topics": [
        "create_weights"
      ]
    },
    {
      "page": "distance_weights",
      "title": "Distance-based Spatial Weights",
      "topics": [
        "distance_weights"
      ]
    },
    {
      "page": "eb_rates",
      "title": "Empirical Bayes(EB) Rate",
      "topics": [
        "eb_rates"
      ]
    },
    {
      "page": "eb_rates_standardization",
      "title": "Empirical Bayes(EB) Rate Standardization",
      "topics": [
        "eb_rates_standardization"
      ]
    },
    {
      "page": "gda_distance_weights",
      "title": "(For internally use and test only) Distance-based Spatial Weights",
      "topics": [
        "gda_distance_weights"
      ]
    },
    {
      "page": "gda_kernel_knn_weights",
      "title": "(For internally use and test only) K-NN Kernel Spatial Weights",
      "topics": [
        "gda_kernel_knn_weights"
      ]
    },
    {
      "page": "gda_kernel_weights",
      "title": "(For internally use and test only) Distance-based Kernel Spatial Weights",
      "topics": [
        "gda_kernel_weights"
      ]
    },
    {
      "page": "gda_knn_weights",
      "title": "(For internally use and test only) K-Nearest Neighbors-based Spatial Weights",
      "topics": [
        "gda_knn_weights"
      ]
    },
    {
      "page": "gda_min_distthreshold",
      "title": "(For internally use and test only) Minimum Distance Threshold for Distance-based Weights",
      "topics": [
        "gda_min_distthreshold"
      ]
    },
    {
      "page": "gda_queen_weights",
      "title": "(For internally use and test only) Queen Contiguity Spatial Weights",
      "topics": [
        "gda_queen_weights"
      ]
    },
    {
      "page": "gda_rook_weights",
      "title": "(For internally use and test only) Rook Contiguity Spatial Weights",
      "topics": [
        "gda_rook_weights"
      ]
    },
    {
      "page": "geoda_open",
      "title": "Create an instance of geoda-class by reading from an ESRI Shapefile dataset",
      "topics": [
        "geoda_open"
      ]
    },
    {
      "page": "geoda-class",
      "title": "'geoda' class",
      "topics": [
        "geoda",
        "geoda-class"
      ]
    },
    {
      "page": "get_neighbors",
      "title": "Neighbors of one observation",
      "topics": [
        "get_neighbors"
      ]
    },
    {
      "page": "get_neighbors_weights",
      "title": "Weights values of the neighbors of one observation",
      "topics": [
        "get_neighbors_weights"
      ]
    },
    {
      "page": "has_isolates",
      "title": "Isolation/Island in Spatial Weights",
      "topics": [
        "has_isolates"
      ]
    },
    {
      "page": "hinge15_breaks",
      "title": "(Box) Hinge15 Breaks",
      "topics": [
        "hinge15_breaks"
      ]
    },
    {
      "page": "hinge30_breaks",
      "title": "(Box) Hinge30 Breaks",
      "topics": [
        "hinge30_breaks"
      ]
    },
    {
      "page": "is_symmetric",
      "title": "Symmetry of Weights Matrix",
      "topics": [
        "is_symmetric"
      ]
    },
    {
      "page": "join_count_ratio",
      "title": "Join Count Ratio",
      "topics": [
        "join_count_ratio"
      ]
    },
    {
      "page": "kernel_knn_weights",
      "title": "K-NN Kernel Spatial Weights",
      "topics": [
        "kernel_knn_weights"
      ]
    },
    {
      "page": "kernel_weights",
      "title": "Distance-based Kernel Spatial Weights",
      "topics": [
        "kernel_weights"
      ]
    },
    {
      "page": "knn_weights",
      "title": "K-Nearest Neighbors-based Spatial Weights",
      "topics": [
        "knn_weights"
      ]
    },
    {
      "page": "lisa_bo",
      "title": "Bonferroni bound value of local spatial autocorrelation",
      "topics": [
        "lisa_bo"
      ]
    },
    {
      "page": "lisa_clusters",
      "title": "Get local cluster indicators",
      "topics": [
        "lisa_clusters"
      ]
    },
    {
      "page": "lisa_colors",
      "title": "Get cluster colors",
      "topics": [
        "lisa_colors"
      ]
    },
    {
      "page": "lisa_fdr",
      "title": "False Discovery Rate value of local spatial autocorrelation",
      "topics": [
        "lisa_fdr"
      ]
    },
    {
      "page": "lisa_labels",
      "title": "Get cluster labels",
      "topics": [
        "lisa_labels"
      ]
    },
    {
      "page": "lisa_num_nbrs",
      "title": "Get numbers of neighbors for all observations",
      "topics": [
        "lisa_num_nbrs"
      ]
    },
    {
      "page": "lisa_pvalues",
      "title": "Get pseudo-p values of LISA",
      "topics": [
        "lisa_pvalues"
      ]
    },
    {
      "page": "lisa_values",
      "title": "Get LISA values",
      "topics": [
        "lisa_values"
      ]
    },
    {
      "page": "LISA-class",
      "title": "LISA class (Internally Used)",
      "topics": [
        "LISA",
        "LISA-class"
      ]
    },
    {
      "page": "local_bijoincount",
      "title": "Bivariate Local Join Count Statistics",
      "topics": [
        "local_bijoincount"
      ]
    },
    {
      "page": "local_bimoran",
      "title": "Bivariate Local Moran Statistics",
      "topics": [
        "local_bimoran"
      ]
    },
    {
      "page": "local_g",
      "title": "Local Getis-Ord's G Statistics",
      "topics": [
        "local_g"
      ]
    },
    {
      "page": "local_geary",
      "title": "Local Geary Statistics",
      "topics": [
        "local_geary"
      ]
    },
    {
      "page": "local_gstar",
      "title": "Local Getis-Ord's G* Statistics",
      "topics": [
        "local_gstar"
      ]
    },
    {
      "page": "local_joincount",
      "title": "Local Join Count Statistics",
      "topics": [
        "local_joincount"
      ]
    },
    {
      "page": "local_moran",
      "title": "Local Moran Statistics",
      "topics": [
        "local_moran"
      ]
    },
    {
      "page": "local_moran_eb",
      "title": "Local Moran with Empirical Bayes(EB) Rate",
      "topics": [
        "local_moran_eb"
      ]
    },
    {
      "page": "local_multigeary",
      "title": "Local Multivariate Geary Statistics",
      "topics": [
        "local_multigeary"
      ]
    },
    {
      "page": "local_multijoincount",
      "title": "(Multivariate) Colocation Local Join Count Statistics",
      "topics": [
        "local_multijoincount"
      ]
    },
    {
      "page": "local_multiquantilelisa",
      "title": "Multivariate Quantile LISA Statistics",
      "topics": [
        "local_multiquantilelisa"
      ]
    },
    {
      "page": "local_quantilelisa",
      "title": "Quantile LISA Statistics",
      "topics": [
        "local_quantilelisa"
      ]
    },
    {
      "page": "make_spatial",
      "title": "Make Spatial",
      "topics": [
        "make_spatial"
      ]
    },
    {
      "page": "max_neighbors",
      "title": "Maximum Neighbors of Spatial Weights",
      "topics": [
        "max_neighbors"
      ]
    },
    {
      "page": "maxp_greedy",
      "title": "A greedy algorithm to solve the max-p-region problem",
      "topics": [
        "maxp_greedy"
      ]
    },
    {
      "page": "maxp_sa",
      "title": "A simulated annealing algorithm to solve the max-p-region problem",
      "topics": [
        "maxp_sa"
      ]
    },
    {
      "page": "maxp_tabu",
      "title": "A tabu-search algorithm to solve the max-p-region problem",
      "topics": [
        "maxp_tabu"
      ]
    },
    {
      "page": "mean_neighbors",
      "title": "Mean Neighbors of Spatial Weights",
      "topics": [
        "mean_neighbors"
      ]
    },
    {
      "page": "median_neighbors",
      "title": "Median Neighbors of Spatial Weights",
      "topics": [
        "median_neighbors"
      ]
    },
    {
      "page": "min_distthreshold",
      "title": "Minimum Distance Threshold for Distance-based Weights",
      "topics": [
        "min_distthreshold"
      ]
    },
    {
      "page": "min_neighbors",
      "title": "Minimum Neighbors of Spatial Weights",
      "topics": [
        "min_neighbors"
      ]
    },
    {
      "page": "natural_breaks",
      "title": "Natural Breaks (Jenks)",
      "topics": [
        "natural_breaks"
      ]
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