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, <http://hdl.handle.net/10919/114845>) for comprehensive
methodological details and <https://bitbucket.org/gramacylab/deepgp-ex/> 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. Gradient-enhancement and gradient
predictions are offered following Booth (2025, <doi:10.48550/arXiv.2512.18066>).
Vecchia approximation for faster
computation is implemented following Sauer, Cooper, and Gramacy
(2023, <doi:10.48550/arXiv.2204.02904>). Optional monotonic warpings are
implemented following Barnett et al. (2025, <doi:10.48550/arXiv.2408.01540>).
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,
2025, <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: |
1.2.0 |
| Depends: |
R (≥ 3.6) |
| Imports: |
grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, fields, Matrix, Rcpp, mvtnorm, FNN, abind |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
interp, knitr, rmarkdown |
| Published: |
2026-01-08 |
| DOI: |
10.32614/CRAN.package.deepgp |
| Author: |
Annie S. Booth [aut, cre] |
| Maintainer: |
Annie S. Booth <annie_booth at vt.edu> |
| License: |
LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| NeedsCompilation: |
yes |
| Materials: |
README |
| CRAN checks: |
deepgp results |
Documentation:
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