Spatial Tessellation, Modeling, and Cross-Validation Toolkit

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Documentation for package ‘spatialkit’ version 1.0.0

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

Help Pages

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
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_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
evaluate_models	Evaluate spatial models (legacy interface)
evaluate_models_cv	Cross-validated comparison with optional tessellation (legacy interface)
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
get_voronoi_seeds	Generate seed points for Voronoi tessellation
gp_lengthscale_bounds	Heuristic length-scale bounds for a squared-exponential GP
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
phi_prior_bounds	Heuristic length-scale bounds for a squared-exponential GP
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
prep_model_data	Prepare and sanitize an sf dataset for spatial modeling
residual_morans_i	Compute Moran's I on the residuals of a fitted spatial model
summarize_by_cell	Summarize features by polygon/cell ID
voronoi_seeds_kmeans	K-means seed generation from point coordinates
voronoi_seeds_random	Random seed generation within a polygonal boundary