This grants the functionality of the Maxar Geospatial Platform (MGP) Streaming API. It can search for images using the WFS method. It can Download images using WMS WMTS. It can also Download a full resolution image.

Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.

Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2021).

Designed to enable simultaneous substitution in strings in a safe fashion.
    Safe means it does not rely on placeholders (which can cause errors in same length matches).

Fits a geographically weighted regression model with different scales
	for each covariate. Uses the negative binomial distribution as default,
	but also accepts the normal, Poisson, or logistic distributions.
	Can fit the global versions of each regression and also the geographically
	weighted alternatives with only one scale, since they are all particular
	cases of the multiscale approach.
	Hanchen Yu (2024). "Exploring Multiscale Geographically Weighted Negative Binomial Regression", Annals of the American Association of Geographers <doi:10.1080/24694452.2023.2289986>.
	Fotheringham AS, Yang W, Kang W (2017). "Multiscale Geographically Weighted Regression (MGWR)", Annals of the American Association of Geographers <doi:10.1080/24694452.2017.1352480>.
	Da Silva AR, Rodrigues TCV (2014). "Geographically Weighted Negative Binomial Regression - incorporating overdispersion", Statistics and Computing <doi:10.1007/s11222-013-9401-9>.