Implements a spatial extension of the random forest algorithm 
             (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
             Allows for a geographically weighted random forest regression 
             including a function to find the optical bandwidth. (Georganos 
             and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).

Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.

Tools to assess the association between two spatial processes. Currently,
  several methodologies are implemented: A modified t-test to perform hypothesis testing
  about the independence between the processes, a suitable nonparametric correlation
  coefficient, the codispersion coefficient, and an F test for assessing the multiple
  correlation between one spatial process and several others. Functions for image
  processing and computing the spatial association between images are also provided.
  Functions contained in the package are intended to accompany Vallejos, R., Osorio, F.,
  Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables:
  With Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.

A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>.  This spatially generated data can be used to test different issues related to the statistical analysis of spatial data.
 This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables.