deepgp: Sequential Design for Deep Gaussian Processes using MCMC

Performs model fitting and sequential design for deep Gaussian processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>. 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. Sequential design criteria include integrated mean-squared error (IMSE), active learning Cohn (ALC), and expected improvement (EI). Applicable to both noisy and deterministic functions. Incorporates SNOW parallelization and utilizes C and C++ under the hood.

Version: 0.3.1
Depends: R (≥ 3.6)
Imports: grDevices, graphics, stats, doParallel, foreach, parallel, Rcpp, mvtnorm
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: akima, knitr
Published: 2021-12-07
Author: Annie Sauer
Maintainer: Annie Sauer <anniees at vt.edu>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: README
CRAN checks: deepgp results

Documentation:

Reference manual: deepgp.pdf

Downloads:

Package source: deepgp_0.3.1.tar.gz
Windows binaries: r-devel: deepgp_0.3.0.zip, r-devel-UCRT: deepgp_0.3.0.zip, r-release: deepgp_0.3.0.zip, r-oldrel: deepgp_0.3.0.zip
macOS binaries: r-release (arm64): deepgp_0.3.1.tgz, r-release (x86_64): deepgp_0.3.0.tgz, r-oldrel: deepgp_0.3.0.tgz
Old sources: deepgp archive

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