Package: deepgp
Type: Package
Title: Bayesian Deep Gaussian Processes using MCMC
Version: 1.1.1
Date: 2023-08-04
Author: Annie S. Booth <annie_booth@ncsu.edu>
Maintainer: Annie S. Booth <annie_booth@ncsu.edu>
Depends: R (>= 3.6)
Description: Performs Bayesian posterior inference for deep Gaussian processes following 
    Sauer, Gramacy, and Higdon (2023, <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.  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, 2023), optimization through expected improvement (EI; 
    Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>), and contour location through entropy
    (Sauer, 2023).  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, Matrix, Rcpp, mvtnorm, FNN
LinkingTo: Rcpp, RcppArmadillo,
Suggests: interp, knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.1.2
Packaged: 2023-08-07 19:18:44 UTC; anniesauer
Repository: CRAN
Date/Publication: 2023-08-07 20:50:05 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 01:35:59 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
