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 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 source code
    is available on www.geotop.org. Technical details about the model are
    available in Endrizzi et al (2014) <https://gmd.copernicus.org/articles/7/2831/2014/gmd-7-2831-2014.html>.

Provides functions and methods for: splitting large raster objects
             into smaller chunks, transferring images from a binary format into raster 
             layers, transferring raster layers into an 'RData' file, calculating the 
             maximum gap (amount of consecutive missing values) of a numeric vector, 
             and fitting harmonic regression models to periodic time series. The homoscedastic
             harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) <doi:10.1080/014311600209814>.