Package: deepgp
Type: Package
Title: Bayesian Deep Gaussian Processes using MCMC
Version: 1.2.3
Date: 2026-07-29
Authors@R: person(given = c("Annie", "S."),
                    family = "Booth",
                    role = c("aut", "cre"),
                    email = "annie_booth@vt.edu")
Depends: R (>= 3.6)
Description: 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.
License: LGPL
Encoding: UTF-8
NeedsCompilation: yes
Imports: grDevices, graphics, stats, doParallel, foreach, parallel,
        GpGp, fields, Matrix, Rcpp, mvtnorm, FNN, abind
LinkingTo: Rcpp, RcppArmadillo,
Suggests: interp, knitr, rmarkdown
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
RoxygenNote: 7.3.3
Packaged: 2026-09-28 09:32:58 UTC; root
Author: Annie S. Booth [aut, cre]
Maintainer: Annie S. Booth <annie_booth@vt.edu>
Repository: https://anniesbooth.r-universe.dev
Date/Publication: 2026-07-30 14:38:22 UTC
RemoteUrl: https://github.com/cran/deepgp
RemoteRef: HEAD
RemoteSha: 5ba6d7c169bd3b2d6b9aabb409880f1525b6ff46