Package: deepgp
Type: Package
Title: Bayesian Deep Gaussian Processes using MCMC
Version: 1.1.2
Date: 2024-04-28
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, <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.  Vecchia-approximation for faster 
    computation is implemented following Sauer, Cooper, and Gramacy 
    (2023, <doi:10.48550/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, 2022 <doi:10.48550/arXiv.2112.07457>), 
    and contour location through entropy 
    (Booth, Renganathan, and Gramacy, 2024 <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, Matrix, Rcpp, mvtnorm, FNN
LinkingTo: Rcpp, RcppArmadillo,
Suggests: interp, knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 7.2.3
Packaged: 2024-04-28 17:42:53 UTC; asauer3
Repository: CRAN
Date/Publication: 2024-04-28 21:10:06 UTC
Built: R 4.3.2; x86_64-apple-darwin20; 2024-04-29 00:56:53 UTC; unix
Archs: deepgp.so.dSYM
