Bayesian Deep Gaussian Processes using MCMC
Overview
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 (2026, 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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-07-31
- ERROR11 OK, 2 ERROR2026-07-30
- OK13 OK2026-05-12
- WARNING12 OK, 1 WARNING2026-05-11
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- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 16includes a package overview page
- Examples that run
- 19%3 run in a plain check · 4 only when donttest examples are enabled (R CMD check --as-cran) · 9 hold no code
- Exported functions with examples
- 100%
- Documented parameters
- 99%
- Return-value docs
- 77%
- Help pages citing references
- 69%
Downloads
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Code & Tests
People & History
Maintainer since 0.1.0 (2020)
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- 1.2.3Latest
- 1.2.22026-07-29 · diff ↗
- RR 4.6.0 released · 2026-04-24
- 1.2.12026-02-09 · diff ↗
- 1.2.0
- RR 4.5.0 released · 2025-04-11
- 1.1.32024-08-19 · diff ↗
- 1.1.22024-04-28 · diff ↗
- RR 4.4.0 released · 2024-04-24
- 1.1.12023-08-07 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 1.1.0
- 1.0.12022-06-20 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 1.0.02022-04-08 · diff ↗
- 0.3.12021-12-07 · diff ↗
Package metadata
- First published
- 2020-10-29
- Total releases
- 15 / 6 yrs
- License
- LGPL
- Minimum R
- ≥ 3.6
- Download size
- 1.2 MB
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