Package: BayesNSGP 0.3.1

BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step. Also provided are nearest-neighbor Gaussian process components for use directly in user-written model code, where the spatial process is retained as a latent field: neighbor-structure construction, a latent-field density and matching simulation function, a purpose-built Metropolis-Hastings sampler that updates the field one node at a time, and posterior prediction at unobserved locations.

Authors:Daniel Turek [aut, cre], Mark Risser [aut], Fabian Ketwaroo [aut]