Spatial Deformation and Dimension Expansion Gaussian Processes
Fitting anisotropic spatial Gaussian process models
Correlation and covariance matrices from censored data
Fitting low-rank nonstationary spatial Gaussian process models through...
Fitting low-rank nonstationary spatial Gaussian process models through...
Plot a fitted deform object
Predict from a fitted deform object
Simulate from a fitted deform object
Plot the variogram for a fitted deform object
Methods for fitting nonstationary Gaussian process models by spatial deformation, as introduced by Sampson and Guttorp (1992) <doi:10.1080/01621459.1992.10475181>, and by dimension expansion, as introduced by Bornn et al. (2012) <doi:10.1080/01621459.2011.646919>. Low-rank thin-plate regression splines, as developed in Wood, S.N. (2003) <doi:10.1111/1467-9868.00374>, are used to either transform co-ordinates or create new latent dimensions.