Computes coordinate transformations of the form (y1, y2) = (f1(x1, x2), f2(x1, x2)) for spatial regression, where a spatial process Y on (y1, y2) has known stationary covariance function. The functions f1 and f2 are obtained via the tensor product of B-splines, with a regularization penalty to ensure f1, f2 are injective functions. The case for Y Gaussian with general covariance function is implemented, as well as documentation for extensions to different spatial covariance functions.
|Author||Guilherme Ludwig [aut, cre], Ronaldo Dias [aut], Paul Sampson [ctb]|
|Maintainer||Guilherme Ludwig <[email protected]nicamp.br>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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