Package: deepgp
Type: Package
Title: Bayesian Deep Gaussian Processes using MCMC
Version: 1.2.0
Date: 2025-12-18
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 (2025, <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
RoxygenNote: 7.3.3
Packaged: 2026-01-08 16:27:10 UTC; anniebooth
Author: Annie S. Booth [aut, cre]
Maintainer: Annie S. Booth <annie_booth@vt.edu>
Repository: CRAN
Date/Publication: 2026-01-08 23:50:09 UTC
Built: R 4.5.2; x86_64-w64-mingw32; 2026-01-21 03:28:37 UTC; windows
Archs: x64
