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) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.
Version: 0.1.3 Depends: R (≥ 3.6.0) Imports: TMB (≥ 1.9.7), numDeriv, rstan, sfsmisc, Matrix (≥ 1.6.3), aghq (≥ 0.4.1), fda, tmbstan, LaplacesDemon, methods LinkingTo: TMB (≥ 1.9.7), RcppEigen Suggests: rmarkdown, knitr, survival, testthat (≥ 3.0.0) Published: 2024-11-12 DOI: 10.32614/CRAN.package.BayesGP Author: Ziang Zhang [aut, cre], Yongwei Lin [aut], Alex Stringer [aut], Patrick Brown [aut] Maintainer: Ziang Zhang <ziangzhang at uchicago.edu> License: GPL (≥ 3) NeedsCompilation: yes Materials: README NEWS CRAN checks: BayesGP results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=BayesGP to link to this page.
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