Spatial Tessellation, Modeling, and Cross-Validation Toolkit

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Documentation for package ‘spatialkit’ version 2.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

area_of_applicability	Area of applicability of a spatial prediction model
assign_features_to_polygons	Assign features to polygons and attach a polygon ID
build_tessellation	Build a tessellation (Voronoi, Delaunay triangles, hex grid, or square grid)
clear_fitted_cache	Clear cached fitted values for a Bayesian spatial model
clear_grid_cache	Clear the in-session grid cache
clip_target_for	Build a polygonal clip target from points and/or a boundary
coef.bayesian_fit	Extract Bayesian model fixed-effect summaries
coef.gwr_fit	Extract GWR local coefficients
coef.rf_fit	Coefficients are undefined for a random forest
coerce_to_points	Coerce arbitrary geometries to representative points
compare_models	Side-by-side comparison of fitted spatial models
compare_models_cv	Cross-validated comparison of spatial models
create_grid_polygons	Create square or hexagonal grid polygons over a boundary
create_grid_polygons_cached	Create and cache grid polygons over a boundary
create_voronoi_polygons	Create Voronoi polygons from points with robust CRS and optional clipping
cv_bayes	K-fold cross-validation for the Bayesian spatial model
cv_gwr	K-fold cross-validation for GWR
cv_rf	Cross-validate a random forest with spatial folds
cv_spatial	Model-agnostic spatial cross-validation
determine_optimal_levels	Determine an optimal number of spatial levels via an elbow heuristic
ensure_projected	Ensure an object has a projected CRS (with sensible defaults)
ensure_stable_poly_id	Create deterministic, stable polygon IDs based on spatial sort keys
estimate_sac_range	Estimate the spatial autocorrelation range from data
evaluate_insample	Compute in-sample (or out-of-sample) metrics for fitted spatial models
fitted.bayesian_fit	In-sample fitted values from a Bayesian spatial GP fit
fitted.gwr_fit	In-sample fitted values from a GWR fit
fitted.rf_fit	Out-of-bag predictions from a random forest fit
fit_bayesian_spatial_model	Fit a Bayesian spatial regression with a 2D Gaussian Process (via brms)
fit_gwr_model	Fit a Geographically Weighted Regression (GWR) via GWmodel
fit_rf_model	Fit a random forest via ranger
get_voronoi_seeds	Generate seed points for Voronoi tessellation
gp_lengthscale_bounds	Heuristic length-scale bounds for a squared-exponential GP
gwr_model_selection	Forward model selection for geographically weighted regression
harmonize_crs	Harmonize CRS between two spatial objects
make_folds	Create spatial cross-validation folds
model_metrics	Compute goodness-of-fit metrics for a spatial model
model_metrics.spatial_fit	Compute goodness-of-fit metrics for a spatial model
new_spatial_fit	Build a spatial_fit S3 object
plot.spatial_fit	Plot a fitted spatial model
plot_folds	Map a cross-validation fold scheme
plot_tessellation_map	Plot a tessellation map with optional boundary, seeds, and features
predict.bayesian_fit	Predict from a Bayesian spatial GP model
predict.gwr_fit	Predict from a GWR spatial model
predict.rf_fit	Predict from a random forest fit
predict_surface	Predict a fitted spatial model onto a regular grid
prep_model_data	Prepare and sanitize an sf dataset for spatial modeling
print.aoa	Print an area-of-applicability result
print.gwr_model_selection	Print a GWR model selection result
print.rf_fit	Print a random forest fit
print.sac_range	Print a spatial autocorrelation range
print.spatial_fit	Print a fitted spatial model
residuals.bayesian_fit	In-sample residuals from a Bayesian spatial GP fit
residuals.gwr_fit	In-sample residuals from a GWR fit
residuals.rf_fit	Out-of-bag residuals from a random forest fit
residual_morans_i	Compute Moran's I on the residuals of a fitted spatial model
select_features_forward	Greedy forward feature selection with spatially blocked inner folds
spatialkit_quiet	Quieten (or restore) spatialkit's console log
summarize_by_cell	Summarize features by polygon/cell ID
summary.spatial_fit	Summarise a fitted spatial model
voronoi_seeds_kmeans	K-means seed generation from point coordinates
voronoi_seeds_random	Random seed generation within a polygonal boundary