Bayesian Analysis of Non-Stationary Gaussian Process Models R package BayesNSGP version 0.2.0 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. Software Depends: R (>= 3.4.0),nimble Imports: FNN,Matrix,methods,StatMatch Daniel Turek <danielturek@gmail.com> Comprehensive R Archive Network (CRAN) Daniel Turek [aut, cre], Mark Risser [aut] GPL-3 2025-12-11 application/tgz https://CRAN.R-project.org/package=BayesNSGP doi:10.32614/CRAN.package.BayesNSGP