Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling using a personal laptop. We furthermore implement approximate Gaussian process inference to account for moderately large spatial data sets. Bayesian inference and posterior prediction is carried out using Markov chain Monte Carlo methods.