Package: spatialkit 2.0.0.9000

spatialkit: Spatial Tessellation, Modeling, and Cross-Validation Toolkit

Constructs analysis regions from the distribution of the data itself, as an alternative to aggregating onto administrative boundaries that were drawn for unrelated purposes. Seeds and builds Voronoi, Delaunay, hexagonal and square tessellations with reproducible identifiers, selects a cell count from the spatial structure of the observations, assigns features to cells, and aggregates to cell level with optional design-effect corrections so that standard errors account for within-cell autocorrelation. Also manages coordinate reference systems. Fits geographically weighted regression (via 'GWmodel'; Lu et al. (2014) <doi:10.1080/10095020.2014.917453>), Bayesian spatial Gaussian process regression (via 'brms', using the Hilbert space approximation of Riutort-Mayol et al. (2023) <doi:10.1007/s11222-022-10167-2>) and random forests (via 'ranger', with the permutation importance of Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>), each behind one S3 class with consistent predict, fitted, residuals and plot methods. Provides spatial cross-validation with random, block, buffered, leave-location-out and nearest-neighbour distance-matched folds (Mila et al. (2022) <doi:10.1111/2041-210X.13851>), forward variable selection, model comparison, prediction onto a regular surface, and the area of applicability of Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650> to flag where a fitted model extrapolates beyond its training data.

Authors:Justin Chase [aut, cre, cph]