deepgp: Deep Gaussian Processes using MCMC

Performs posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. 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 (2022) <arXiv:2204.02904>. Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2020) and optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Covariance kernel options are matern (default) and squared exponential. Applicable to both noisy and deterministic functions. Incorporates SNOW parallelization and utilizes C and C++ under the hood.

Version: 1.0.1
Depends: R (≥ 3.6)
Imports: grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, Matrix, Rcpp, mvtnorm, FNN
LinkingTo: Rcpp, RcppArmadillo
Suggests: interp, knitr, rmarkdown
Published: 2022-06-20
Author: Annie Sauer
Maintainer: Annie Sauer <anniees at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: README
CRAN checks: deepgp results


Reference manual: deepgp.pdf


Package source: deepgp_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deepgp_1.0.1.tgz, r-oldrel (arm64): deepgp_1.0.1.tgz, r-release (x86_64): deepgp_1.0.1.tgz, r-oldrel (x86_64): deepgp_1.0.1.tgz
Old sources: deepgp archive


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