{
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  "Title": "Ergonomic Methods for Assessing Spatial Models",
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  "Authors@R": "c(\nperson(\"Michael\", \"Mahoney\", , \"mike.mahoney.218@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2402-304X\")),\nperson(\"Lucas\", \"Johnson\", , \"lucas.k.johnson03@gmail.com\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-7953-0260\")),\nperson(\"Virgilio\", \"Gómez-Rubio\", role = \"rev\",\ncomment = \"Virgilio reviewed the package (v. 0.2.0.9000) for rOpenSci, see <https://github.com/ropensci/software-review/issues/571>\"),\nperson(\"Jakub\", \"Nowosad\", role = \"rev\",\ncomment = \"Jakub reviewed the package (v. 0.2.0.9000) for rOpenSci, see <https://github.com/ropensci/software-review/issues/571>\"),\nperson(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"))\n)",
  "Description": "Assessing predictive models of spatial data can be\nchallenging, both because these models are typically built for\nextrapolating outside the original region represented by\ntraining data and due to potential spatially structured errors,\nwith \"hot spots\" of higher than expected error clustered\ngeographically due to spatial structure in the underlying data.\nMethods are provided for assessing models fit to spatial data,\nincluding approaches for measuring the spatial structure of\nmodel errors, assessing model predictions at multiple spatial\nscales, and evaluating where predictions can be made safely.\nMethods are particularly useful for models fit using the\n'tidymodels' framework. Methods include Moran's I ('Moran'\n(1950) <doi:10.2307/2332142>), Geary's C ('Geary' (1954)\n<doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995)\n<doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement\ncoefficients from 'Ji' and Gallo (2006) (<doi:\n10.14358/PERS.72.7.823>), agreement metrics from 'Willmott'\n(1981) (<doi: 10.1080/02723646.1981.10642213>) and 'Willmott'\n'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an implementation\nof the area of applicability methodology from 'Meyer' and\n'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an\nimplementation of multi-scale assessment as described in\n'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).",
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  "Repository": "https://ropensci.r-universe.dev",
  "Date/Publication": "2025-04-15 23:57:33 UTC",
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  "Author": "Michael Mahoney [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2402-304X>),\nLucas Johnson [ctb] (ORCID: <https://orcid.org/0000-0002-7953-0260>),\nVirgilio Gómez-Rubio [rev] (Virgilio reviewed the package (v.\n0.2.0.9000) for rOpenSci, see\n<https://github.com/ropensci/software-review/issues/571>),\nJakub Nowosad [rev] (Jakub reviewed the package (v. 0.2.0.9000) for\nrOpenSci, see\n<https://github.com/ropensci/software-review/issues/571>),\nPosit Software, PBC [cph, fnd]",
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    "ww_agreement_coefficient_vec",
    "ww_area_of_applicability",
    "ww_build_neighbors",
    "ww_build_weights",
    "ww_global_geary_c",
    "ww_global_geary_c_vec",
    "ww_global_geary_pvalue",
    "ww_global_geary_pvalue_vec",
    "ww_global_moran_i",
    "ww_global_moran_i_vec",
    "ww_global_moran_pvalue",
    "ww_global_moran_pvalue_vec",
    "ww_local_geary_c",
    "ww_local_geary_c_vec",
    "ww_local_geary_pvalue",
    "ww_local_geary_pvalue_vec",
    "ww_local_getis_ord_g",
    "ww_local_getis_ord_g_pvalue",
    "ww_local_getis_ord_g_pvalue_vec",
    "ww_local_getis_ord_g_vec",
    "ww_local_moran_i",
    "ww_local_moran_i_vec",
    "ww_local_moran_pvalue",
    "ww_local_moran_pvalue_vec",
    "ww_make_point_neighbors",
    "ww_make_polygon_neighbors",
    "ww_multi_scale",
    "ww_systematic_agreement_coefficient",
    "ww_systematic_agreement_coefficient_vec",
    "ww_systematic_mpd",
    "ww_systematic_mpd_vec",
    "ww_systematic_mse",
    "ww_systematic_mse_vec",
    "ww_systematic_rmpd",
    "ww_systematic_rmpd_vec",
    "ww_systematic_rmse",
    "ww_systematic_rmse_vec",
    "ww_unsystematic_agreement_coefficient",
    "ww_unsystematic_agreement_coefficient_vec",
    "ww_unsystematic_mpd",
    "ww_unsystematic_mpd_vec",
    "ww_unsystematic_mse",
    "ww_unsystematic_mse_vec",
    "ww_unsystematic_rmpd",
    "ww_unsystematic_rmpd_vec",
    "ww_unsystematic_rmse",
    "ww_unsystematic_rmse_vec",
    "ww_willmott_d",
    "ww_willmott_d_vec",
    "ww_willmott_d1",
    "ww_willmott_d1_vec",
    "ww_willmott_dr",
    "ww_willmott_dr_vec"
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      "title": "Guerry \"Moral Statistics\" (1830s)",
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        "data.frame"
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        "Donatns",
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        "Lottery",
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        "data.frame"
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      "page": "guerry",
      "title": "Guerry \"Moral Statistics\" (1830s)",
      "topics": [
        "guerry"
      ]
    },
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      "page": "ny_trees",
      "title": "Number of trees and aboveground biomass for Forest Inventory and Analysis plots in New York State",
      "topics": [
        "ny_trees"
      ]
    },
    {
      "page": "predict.ww_area_of_applicability",
      "title": "Predict from a 'ww_area_of_applicability'",
      "concept": "area of applicability functions",
      "topics": [
        "predict.ww_area_of_applicability"
      ]
    },
    {
      "page": "worldclim_simulation",
      "title": "Simulated data based on WorldClim Bioclimatic variables",
      "topics": [
        "worldclim_simulation"
      ]
    },
    {
      "page": "ww_agreement_coefficient",
      "title": "Agreement coefficients and related methods",
      "concept": "agreement metrics",
      "topics": [
        "ww_agreement_coefficient",
        "ww_agreement_coefficient.data.frame",
        "ww_agreement_coefficient_vec",
        "ww_systematic_agreement_coefficient",
        "ww_systematic_agreement_coefficient.data.frame",
        "ww_systematic_agreement_coefficient_vec",
        "ww_systematic_mpd",
        "ww_systematic_mpd.data.frame",
        "ww_systematic_mpd_vec",
        "ww_systematic_rmpd",
        "ww_systematic_rmpd.data.frame",
        "ww_systematic_rmpd_vec",
        "ww_unsystematic_agreement_coefficient",
        "ww_unsystematic_agreement_coefficient.data.frame",
        "ww_unsystematic_agreement_coefficient_vec",
        "ww_unsystematic_mpd",
        "ww_unsystematic_mpd.data.frame",
        "ww_unsystematic_mpd_vec",
        "ww_unsystematic_rmpd",
        "ww_unsystematic_rmpd.data.frame",
        "ww_unsystematic_rmpd_vec"
      ]
    },
    {
      "page": "ww_area_of_applicability",
      "title": "Find the area of applicability",
      "concept": "area of applicability functions",
      "topics": [
        "ww_area_of_applicability",
        "ww_area_of_applicability.data.frame",
        "ww_area_of_applicability.formula",
        "ww_area_of_applicability.matrix",
        "ww_area_of_applicability.recipe",
        "ww_area_of_applicability.rset"
      ]
    },
    {
      "page": "ww_build_neighbors",
      "title": "Make 'nb' objects from sf objects",
      "topics": [
        "ww_build_neighbors"
      ]
    },
    {
      "page": "ww_build_weights",
      "title": "Build \"listw\" objects of spatial weights",
      "topics": [
        "ww_build_weights"
      ]
    },
    {
      "page": "global_geary_c",
      "title": "Global Geary's C statistic",
      "concept": "autocorrelation metrics",
      "topics": [
        "ww_global_geary_c",
        "ww_global_geary_c_vec",
        "ww_global_geary_pvalue",
        "ww_global_geary_pvalue_vec"
      ]
    },
    {
      "page": "global_moran_i",
      "title": "Global Moran's I statistic",
      "concept": "autocorrelation metrics",
      "topics": [
        "ww_global_moran_i",
        "ww_global_moran_i_vec",
        "ww_global_moran_pvalue",
        "ww_global_moran_pvalue_vec"
      ]
    },
    {
      "page": "local_geary_c",
      "title": "Local Geary's C statistic",
      "concept": "autocorrelation metrics",
      "topics": [
        "ww_local_geary_c",
        "ww_local_geary_c_vec",
        "ww_local_geary_pvalue",
        "ww_local_geary_pvalue_vec"
      ]
    },
    {
      "page": "local_getis_ord_g",
      "title": "Local Getis-Ord G and G* statistic",
      "concept": "autocorrelation metrics",
      "topics": [
        "ww_local_getis_ord_g",
        "ww_local_getis_ord_g_pvalue",
        "ww_local_getis_ord_g_pvalue_vec",
        "ww_local_getis_ord_g_vec"
      ]
    },
    {
      "page": "local_moran_i",
      "title": "Local Moran's I statistic",
      "concept": "autocorrelation metrics",
      "topics": [
        "ww_local_moran_i",
        "ww_local_moran_i_vec",
        "ww_local_moran_pvalue",
        "ww_local_moran_pvalue_vec"
      ]
    },
    {
      "page": "ww_make_point_neighbors",
      "title": "Make 'nb' objects from point geometries",
      "topics": [
        "ww_make_point_neighbors"
      ]
    },
    {
      "page": "ww_make_polygon_neighbors",
      "title": "Make 'nb' objects from polygon geometries",
      "topics": [
        "ww_make_polygon_neighbors"
      ]
    },
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