deepgp: Bayesian Deep Gaussian Processes using MCMC

Performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>). See Sauer (2023, <>) for comprehensive methodological details and <> for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Vecchia-approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, <doi:10.48550/arXiv.2204.02904>). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022 <doi:10.48550/arXiv.2112.07457>), and contour location through entropy (Booth, Renganathan, and Gramacy, 2024 <doi:10.48550/arXiv.2308.04420>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.

Version: 1.1.2
Depends: R (≥ 3.6)
Imports: grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, Matrix, Rcpp, mvtnorm, FNN
LinkingTo: Rcpp, RcppArmadillo
Suggests: interp, knitr, rmarkdown
Published: 2024-04-28
DOI: 10.32614/CRAN.package.deepgp
Author: Annie S. Booth
Maintainer: Annie S. Booth <annie_booth at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: README
CRAN checks: deepgp results


Reference manual: deepgp.pdf
Vignettes: deepgp


Package source: deepgp_1.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deepgp_1.1.2.tgz, r-oldrel (arm64): deepgp_1.1.2.tgz, r-release (x86_64): deepgp_1.1.2.tgz, r-oldrel (x86_64): deepgp_1.1.2.tgz
Old sources: deepgp archive


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