BayesGP: Efficient Implementation of Gaussian Process in Bayesian Hierarchical Models

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:

Reference manual:	BayesGP.pdf
Vignettes:	BayesGP: Partial Likelihood (source, R code) BayesGP: COVID-19 Example (source, R code) BayesGP: Fitting sGP (source, R code)

Downloads:

Package source:	BayesGP_0.1.3.tar.gz
Windows binaries:	r-devel: BayesGP_0.1.3.zip, r-release: BayesGP_0.1.3.zip, r-oldrel: BayesGP_0.1.3.zip
macOS binaries:	r-release (arm64): BayesGP_0.1.3.tgz, r-oldrel (arm64): BayesGP_0.1.3.tgz, r-release (x86_64): BayesGP_0.1.3.tgz, r-oldrel (x86_64): BayesGP_0.1.3.tgz

Linking:

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