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: 2026-07-05 12:03:58 UTC; root
Author: Daniel Turek [aut, cre], Mark Risser [aut]
Config/pak/sysreqs: libglpk-dev make libxml2-dev
Repository: https://danielturek.r-universe.dev
Date/Publication: 2025-12-11 16:50:55 UTC
RemoteUrl: https://github.com/cran/BayesNSGP
RemoteRef: HEAD
RemoteSha: 25753e5c2e891be465f44e2f757934dcb9ef1e33