Provides an interface to the Maxar Geospatial Platform (MGP) Application Programming Interface. <https://www.maxar.com/maxar-geospatial-platform>
    It facilitates imagery searches using the MGP Streaming Application Programming Interface via the Web Feature Service (WFS) method, and supports image downloads through Web Map Service (WMS) and Web Map Tile Service (WMTS)
    Open Geospatial Consortium (OGC) methods. 
    Additionally, it integrates with the Maxar Geospatial Platform Basemaps Application Programming Interface for accessing Maxar basemaps imagery and seamlines. 
    The package also offers seamless integration with the Maxar Geospatial Platform Discovery Application Programming Interface, allowing users to search, filter, and sort Maxar content, 
    while retrieving detailed metadata in formats like SpatioTemporal Asset Catalog (STAC) and GeoJSON.

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>.

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>.