  Gaussian processes (GP) are a popular and powerful tool for spatial modelling
of data, especially data that quantify environmental processes. However, in
stationary form, whether covariance is isotropic or anisotropic, GPs may lack
the flexibility to capture dependence across a continuous spatial process,
especially across a large domains. The deform package aims to provide users
with user-friendly R functions for the fitting and visualization of
nonstationary spatial Gaussian processes. Users can choose to capture
nonstationarity with either the spatial deformation approach of Sampson and
Guttorp (1992) or the dimension expansion approach of Bornn, Shaddick, and
Zidek (2012). Thin plate regression splines are used for both approaches to
bring transformations of locations to give a new set of locations that bring
isotropic covariance. Fitted models in deform can be used to predict these new
locations and to simulate nonstationary Gaussian processes for an arbitrary set
of locations.
