Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector'
    objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian
    geostatistical models use Maximum Likelihood Estimation.  For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The 'RandomFields' package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.

Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions.

Provides efficient geospatial thinning algorithms to reduce
    the density of coordinate data while maintaining spatial
    relationships. Implements K-D Tree and brute-force distance-based
    thinning, as well as grid-based and precision-based thinning methods.
    For more information on the methods, see Elseberg et al. (2012)
    <https://hdl.handle.net/10446/86202>.

The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.

It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.x/3.x are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model can be found in the provided documentation.

Connecting spatiotemporal exposure to individual and
    population-level risk via source-to-outcome continuum modeling. The package,
    methods, and case-studies are described in Messier, Reif, and Marvel (2025)
    <doi:10.1186/s40246-024-00711-8> and Eccles et al. (2023)
    <doi:10.1016/j.scitotenv.2022.158905>.