Package: BayesNSGP
Title: Bayesian Analysis of Non-Stationary Gaussian Process Models
Description: 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.
Version: 0.2.0
Date: 2025-12-11
Maintainer: Daniel Turek <danielturek@gmail.com>
Authors@R: c(person("Daniel", "Turek", role = c("aut", "cre"), email = "danielturek@gmail.com"),
             person("Mark", "Risser",  role = "aut"))
Depends: R (>= 3.4.0),nimble
Imports: FNN,Matrix,methods,StatMatch
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-12-11 12:00:21 UTC; dturek
Author: Daniel Turek [aut, cre],
  Mark Risser [aut]
Repository: CRAN
Date/Publication: 2025-12-11 16:20:21 UTC
Built: R 4.6.0; ; 2026-04-26 16:29:11 UTC; unix
