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. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.

Version: 1.1.0
Depends: R (≥ 3.6)
Imports: grDevices, graphics, stats, doParallel, foreach, parallel, GpGp, Matrix, Rcpp, mvtnorm, FNN
LinkingTo: Rcpp, RcppArmadillo
Suggests: interp, knitr, rmarkdown
Published: 2022-12-15
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
Vignettes: deepgp


Package source: deepgp_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deepgp_1.1.0.tgz, r-oldrel (arm64): deepgp_1.1.0.tgz, r-release (x86_64): deepgp_1.1.0.tgz, r-oldrel (x86_64): deepgp_1.1.0.tgz
Old sources: deepgp archive


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