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>.

Implements functional principal component analysis (FPCA) for
    univariate and multivariate sparse functional data. The package estimates
    eigenfunctions, eigenvalues, and error variance simultaneously via maximum
    likelihood estimation (MLE), using a spline basis representation of the
    eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced
    through a modified Gram-Schmidt (MGS) orthogonalization procedure applied
    iteratively during estimation, avoiding direct optimization over the Stiefel
    manifold and improving numerical stability. The optimal number of basis
    functions and principal components is selected via an Akaike Information
    Criterion (AIC)-type criterion, supporting both a full grid-search strategy
    and a computationally efficient sequential selection approach. Principal
    component scores are estimated by conditional expectation, enabling
    reconstruction of individual trajectories over the entire domain from sparse
    observations. Pointwise confidence intervals for reconstructed trajectories 
    are also provided. Methods are described in Mbaka, Cao and Carey
    (2026) <doi:10.48550/arXiv.2603.18833> and Mbaka and Carey (2026)
    <doi:10.48550/arXiv.2603.19799>.

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>.