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: 0.3.0
Date: 2026-08-19
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"),
             person("Fabian", "Ketwaroo", role = "aut"))
Depends: R (>= 3.4.0),nimble
Imports: FNN,Matrix,methods,StatMatch,sf,ggplot2
License: GPL-3
Encoding: UTF-8
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-08-19 13:57:21 UTC; turekd
Author: Daniel Turek [aut, cre],
  Mark Risser [aut],
  Fabian Ketwaroo [aut]
Repository: CRAN
Date/Publication: 2026-08-19 17:50:02 UTC
Built: R 4.5.2; ; 2026-08-19 20:22:08 UTC; unix
