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  "Title": "Easy Spatial Modeling with Random Forest",
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  "Description": "Automatic generation and selection of spatial predictors\nfor Random Forest models fitted to spatially structured data.\nSpatial predictors are constructed from a distance matrix among\ntraining samples using Moran's Eigenvector Maps (MEMs; Dray,\nLegendre, and Peres-Neto 2006\n<DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach\n(Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are\nused alongside user-supplied explanatory variables in Random\nForest models. The package provides functions for model\nfitting, multicollinearity reduction, interaction\nidentification, hyperparameter tuning, evaluation via spatial\ncross-validation, and result visualization using partial\ndependence and interaction plots. Model fitting relies on the\n'ranger' package (Wright and Ziegler 2017\n<DOI:10.18637/jss.v077.i01>).",
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        "bias_area_km2",
        "bias_species_per_record",
        "climate_aridity_index_average",
        "climate_hypervolume",
        "climate_velocity_lgm_average",
        "neighbors_count",
        "neighbors_percent_shared_edge",
        "human_population_density",
        "topography_elevation_average",
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        "fragmentation_division",
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      "tojson": false
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      "concept": [
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    {
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      "concept": [
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      "concept": [
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      "topics": [
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      "topics": [
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      "concept": [
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      "topics": [
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      "concept": [
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    },
    {
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      "title": "Example fitted spatial random forest model",
      "concept": [
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      "topics": [
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    {
      "page": "plants_xy",
      "title": "Coordinates for plant richness data",
      "concept": [
        "data"
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      "topics": [
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      "title": "Visualize spatial cross-validation results",
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      "topics": [
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      "page": "plot_importance",
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    },
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      "page": "plot_moran",
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