  This paper describes and illustrates new functionality for fitting spatially
varying coefficients models in the spBayes (version 0.4-2) R package. The new
spSVC function uses a computationally efficient Markov chain Monte Carlo
algorithm and extends current spBayes functions, that fit only space-varying
intercept regression models, to fit independent or multivariate Gaussian
process random effects for any set of columns in the regression design matrix.
Newly added OpenMP parallelization options for spSVC are discussed and
illustrated, as well as helper functions for joint and point-wise prediction
and model fit diagnostics. The utility of the proposed models is illustrated
using a PM10 analysis over central Europe.
