Package: BayesGP Type: Package Title: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models Version: 0.1.3 Authors@R: c(person(given = "Ziang", family = "Zhang", role = c("aut", "cre"), email = "ziangzhang@uchicago.edu"), person(given = "Yongwei", family = "Lin", role = "aut"), person(given = "Alex", family = "Stringer", role = "aut"), person(given = "Patrick", family = "Brown", role = "aut")) Description: Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) ; Zhang, Stringer, Brown, and Stafford (2024) ; Zhang, Brown, and Stafford (2023) ; and Stringer, Brown, and Stafford (2021) . License: GPL (>= 3) Encoding: UTF-8 LazyData: true RoxygenNote: 7.3.2 LinkingTo: TMB (>= 1.9.7), RcppEigen Imports: TMB (>= 1.9.7), numDeriv, rstan, sfsmisc, Matrix (>= 1.6.3), aghq (>= 0.4.1), fda, tmbstan, LaplacesDemon, methods Depends: R (>= 3.6.0) Suggests: rmarkdown, knitr, survival, testthat (>= 3.0.0) VignetteBuilder: knitr NeedsCompilation: yes Packaged: 2026-10-05 10:15:37 UTC; root Config/testthat/edition: 3 Config/pak/sysreqs: cmake make Repository: https://bayes-gp.r-universe.dev Date/Publication: 2024-12-05 02:36:30 UTC RemoteUrl: https://github.com/bayes-gp/bayesgp RemoteRef: HEAD RemoteSha: 7db1d65825a795f0e74d25fc95d053ce96a0aff4 Author: Ziang Zhang [aut, cre], Yongwei Lin [aut], Alex Stringer [aut], Patrick Brown [aut] Maintainer: Ziang Zhang