{
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  "Package": "spatialkit",
  "Title": "Spatial Tessellation, Modeling, and Cross-Validation Toolkit",
  "Version": "2.0.0.9000",
  "Authors@R": "person(\"Justin\", \"Chase\", email = \"jchase.msu@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "Constructs analysis regions from the distribution of the\ndata itself, as an alternative to aggregating onto\nadministrative boundaries that were drawn for unrelated\npurposes. Seeds and builds Voronoi, Delaunay, hexagonal and\nsquare tessellations with reproducible identifiers, selects a\ncell count from the spatial structure of the observations,\nassigns features to cells, and aggregates to cell level with\noptional design-effect corrections so that standard errors\naccount for within-cell autocorrelation. Also manages\ncoordinate reference systems. Fits geographically weighted\nregression (via 'GWmodel'; Lu et al. (2014)\n<doi:10.1080/10095020.2014.917453>), Bayesian spatial Gaussian\nprocess regression (via 'brms', using the Hilbert space\napproximation of Riutort-Mayol et al. (2023)\n<doi:10.1007/s11222-022-10167-2>) and random forests (via\n'ranger', with the permutation importance of Strobl et al.\n(2007) <doi:10.1186/1471-2105-8-25>), each behind one S3 class\nwith consistent predict, fitted, residuals and plot methods.\nProvides spatial cross-validation with random, block, buffered,\nleave-location-out and nearest-neighbour distance-matched folds\n(Mila et al. (2022) <doi:10.1111/2041-210X.13851>), forward\nvariable selection, model comparison, prediction onto a regular\nsurface, and the area of applicability of Meyer and Pebesma\n(2021) <doi:10.1111/2041-210X.13650> to flag where a fitted\nmodel extrapolates beyond its training data.",
  "License": "MIT + file LICENSE",
  "URL": "https://elkronos.github.io/gis_modeling_toolkit/,\nhttps://github.com/elkronos/gis_modeling_toolkit",
  "BugReports": "https://github.com/elkronos/gis_modeling_toolkit/issues",
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  "Repository": "https://elkronos.r-universe.dev",
  "Date/Publication": "2026-10-07 22:07:41 UTC",
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  "Author": "Justin Chase [aut, cre, cph]",
  "Maintainer": "Justin Chase <jchase.msu@gmail.com>",
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    "assign_features_to_polygons",
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    "clear_fitted_cache",
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    "clip_target_for",
    "coerce_to_points",
    "compare_models",
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    "create_grid_polygons",
    "create_grid_polygons_cached",
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    "cv_block_size_sweep",
    "cv_gwr",
    "cv_rf",
    "cv_spatial",
    "determine_optimal_levels",
    "ensure_projected",
    "ensure_stable_poly_id",
    "estimate_sac_range",
    "evaluate_insample",
    "fit_bayesian_spatial_model",
    "fit_gwr_model",
    "fit_rf_model",
    "fold_separation",
    "get_voronoi_seeds",
    "gp_lengthscale_bounds",
    "gwr_model_selection",
    "harmonize_crs",
    "kriging_adequacy",
    "make_folds",
    "model_metrics",
    "new_spatial_fit",
    "plot_calibration",
    "plot_cv_metrics",
    "plot_folds",
    "plot_tessellation_map",
    "predict_surface",
    "prep_model_data",
    "residual_morans_i",
    "resolution_profile",
    "sac_nugget",
    "select_features_forward",
    "select_resolution",
    "spatialkit_quiet",
    "summarize_by_cell",
    "voronoi_seeds_kmeans",
    "voronoi_seeds_random"
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      "page": "sub-.spatialkit_rows",
      "title": "Subset a layer that carries a row record",
      "topics": [
        "[.spatialkit_rows"
      ]
    },
    {
      "page": "area_of_applicability",
      "title": "Area of applicability of a spatial prediction model",
      "concept": [
        "cross-validation",
        "prediction"
      ],
      "topics": [
        "area_of_applicability"
      ]
    },
    {
      "page": "assign_features_to_polygons",
      "title": "Assign features to polygons and attach a polygon ID",
      "concept": [
        "aggregation"
      ],
      "topics": [
        "assign_features_to_polygons"
      ]
    },
    {
      "page": "build_tessellation",
      "title": "Build a tessellation (Voronoi, Delaunay triangles, hex grid, or square grid)",
      "concept": [
        "tessellation"
      ],
      "topics": [
        "build_tessellation"
      ]
    },
    {
      "page": "clear_fitted_cache",
      "title": "Clear cached fitted values for a Bayesian spatial model",
      "concept": [
        "package options and caches"
      ],
      "topics": [
        "clear_fitted_cache"
      ]
    },
    {
      "page": "clear_grid_cache",
      "title": "Clear the in-session grid cache",
      "concept": [
        "package options and caches"
      ],
      "topics": [
        "clear_grid_cache"
      ]
    },
    {
      "page": "clip_target_for",
      "title": "Build a polygonal clip target from points and/or a boundary",
      "concept": [
        "spatial data preparation"
      ],
      "topics": [
        "clip_target_for"
      ]
    },
    {
      "page": "coef.bayesian_fit",
      "title": "Extract Bayesian model fixed-effect summaries",
      "concept": [
        "methods on a fitted model"
      ],
      "topics": [
        "coef.bayesian_fit"
      ]
    },
    {
      "page": "coef.gwr_fit",
      "title": "Extract GWR local coefficients",
      "concept": [
        "methods on a fitted model"
      ],
      "topics": [
        "coef.gwr_fit"
      ]
    },
    {
      "page": "coef.rf_fit",
      "title": "Coefficients are undefined for a random forest",
      "concept": [
        "methods on a fitted model"
      ],
      "topics": [
        "coef.rf_fit"
      ]
    },
    {
      "page": "coerce_to_points",
      "title": "Coerce arbitrary geometries to representative points",
      "concept": [
        "spatial data preparation"
      ],
      "topics": [
        "coerce_to_points"
      ]
    },
    {
      "page": "compare_models",
      "title": "Side-by-side comparison of fitted spatial models",
      "concept": [
        "model evaluation"
      ],
      "topics": [
        "compare_models"
      ]
    },
    {
      "page": "compare_models_cv",
      "title": "Cross-validated comparison of spatial models",
      "concept": [
        "model evaluation"
      ],
      "topics": [
        "compare_models_cv"
      ]
    },
    {
      "page": "create_grid_polygons",
      "title": "Create square or hexagonal grid polygons over a boundary",
      "concept": [
        "tessellation"
      ],
      "topics": [
        "create_grid_polygons"
      ]
    },
    {
      "page": "create_grid_polygons_cached",
      "title": "Create and cache grid polygons over a boundary",
      "concept": [
        "package options and caches",
        "tessellation"
      ],
      "topics": [
        "create_grid_polygons_cached"
      ]
    },
    {
      "page": "create_voronoi_polygons",
      "title": "Create Voronoi polygons from points with CRS handling and optional clipping",
      "concept": [
        "tessellation"
      ],
      "topics": [
        "create_voronoi_polygons"
      ]
    },
    {
      "page": "cv_bayes",
      "title": "K-fold cross-validation for the Bayesian spatial model",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "cv_bayes"
      ]
    },
    {
      "page": "cv_block_size_sweep",
      "title": "Cross-validate at a ladder of block sizes",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "cv_block_size_sweep"
      ]
    },
    {
      "page": "cv_gwr",
      "title": "K-fold cross-validation for GWR",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "cv_gwr"
      ]
    },
    {
      "page": "cv_rf",
      "title": "Cross-validate a random forest with spatial folds",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "cv_rf"
      ]
    },
    {
      "page": "cv_spatial",
      "title": "Model-agnostic spatial cross-validation",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "cv_spatial"
      ]
    },
    {
      "page": "determine_optimal_levels",
      "title": "Determine an optimal number of spatial levels via an elbow heuristic",
      "concept": [
        "aggregation"
      ],
      "topics": [
        "determine_optimal_levels"
      ]
    },
    {
      "page": "ensure_projected",
      "title": "Ensure an object has a projected CRS (with sensible defaults)",
      "concept": [
        "spatial data preparation"
      ],
      "topics": [
        "ensure_projected"
      ]
    },
    {
      "page": "ensure_stable_poly_id",
      "title": "Create deterministic, stable polygon IDs based on spatial sort keys",
      "concept": [
        "tessellation"
      ],
      "topics": [
        "ensure_stable_poly_id"
      ]
    },
    {
      "page": "estimate_sac_range",
      "title": "Estimate the spatial autocorrelation range from data",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "estimate_sac_range"
      ]
    },
    {
      "page": "evaluate_insample",
      "title": "Compute in-sample (or out-of-sample) metrics for fitted spatial models",
      "concept": [
        "model evaluation"
      ],
      "topics": [
        "evaluate_insample"
      ]
    },
    {
      "page": "fit_bayesian_spatial_model",
      "title": "Fit a Bayesian spatial regression with a 2D Gaussian Process (via brms)",
      "concept": [
        "model fitting"
      ],
      "topics": [
        "fit_bayesian_spatial_model"
      ]
    },
    {
      "page": "fit_gwr_model",
      "title": "Fit a Geographically Weighted Regression (GWR) via GWmodel",
      "concept": [
        "model fitting"
      ],
      "topics": [
        "fit_gwr_model"
      ]
    },
    {
      "page": "fit_rf_model",
      "title": "Fit a random forest via ranger",
      "concept": [
        "model fitting"
      ],
      "topics": [
        "fit_rf_model"
      ]
    },
    {
      "page": "fitted.bayesian_fit",
      "title": "In-sample fitted values from a Bayesian spatial GP fit",
      "concept": [
        "methods on a fitted model"
      ],
      "topics": [
        "fitted.bayesian_fit"
      ]
    },
    {
      "page": "fitted.gwr_fit",
      "title": "In-sample fitted values from a GWR fit",
      "concept": [
        "methods on a fitted model"
      ],
      "topics": [
        "fitted.gwr_fit"
      ]
    },
    {
      "page": "fitted.rf_fit",
      "title": "Out-of-bag predictions from a random forest fit",
      "concept": [
        "methods on a fitted model"
      ],
      "topics": [
        "fitted.rf_fit"
      ]
    },
    {
      "page": "fold_separation",
      "title": "How far the held-out points actually sit from the training data",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "fold_separation"
      ]
    },
    {
      "page": "get_voronoi_seeds",
      "title": "Generate seed points for Voronoi tessellation",
      "concept": [
        "tessellation"
      ],
      "topics": [
        "get_voronoi_seeds"
      ]
    },
    {
      "page": "gp_lengthscale_bounds",
      "title": "Heuristic length-scale bounds for a squared-exponential GP",
      "concept": [
        "model fitting"
      ],
      "topics": [
        "gp_lengthscale_bounds"
      ]
    },
    {
      "page": "gwr_model_selection",
      "title": "Forward model selection for geographically weighted regression",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "gwr_model_selection"
      ]
    },
    {
      "page": "harmonize_crs",
      "title": "Harmonize CRS between two spatial objects",
      "concept": [
        "spatial data preparation"
      ],
      "topics": [
        "harmonize_crs"
      ]
    },
    {
      "page": "kriging_adequacy",
      "title": "Block-kriging adequacy diagnostics for a set of cells",
      "concept": [
        "aggregation"
      ],
      "topics": [
        "kriging_adequacy"
      ]
    },
    {
      "page": "make_folds",
      "title": "Create spatial cross-validation folds",
      "concept": [
        "cross-validation"
      ],
      "topics": [
        "make_folds"
      ]
    },
    {
      "page": "model_metrics",
      "title": "Compute goodness-of-fit metrics for a spatial model",
      "concept": [
        "methods on a fitted model",
        "model evaluation"
      ],
      "topics": [
        "model_metrics",
        "model_metrics.spatial_fit"
      ]
    },
    {
      "page": "new_spatial_fit",
      "title": "Build a spatial_fit S3 object",
      "concept": [
        "model fitting"
      ],
      "topics": [
        "new_spatial_fit"
      ]
    },
    {
      "page": "plot_calibration",
      "title": "Plot the interval calibration of a Bayesian cross-validation",
      "concept": [
        "plotting"
      ],
      "topics": [
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