Package: deepgp
Type: Package
Title: Deep Gaussian Processes using MCMC
Version: 1.0.0
Date: 2022-4-04
Author: Annie Sauer <anniees@vt.edu>
Maintainer: Annie Sauer <anniees@vt.edu>
Depends: R (>= 3.6)
Description: Performs posterior inference for deep Gaussian processes following 
    Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.  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, 2020) and optimization through expected improvement (EI; 
    Gramacy, Sauer, and Wycoff, 2021 <arXiv:2112.07457>).  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.  Applicable to both noisy and deterministic functions.  
    Incorporates SNOW parallelization and utilizes C and 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: akima, knitr
RoxygenNote: 7.1.2
Packaged: 2022-04-08 13:17:27 UTC; anniesauer
Repository: CRAN
Date/Publication: 2022-04-08 14:12:32 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 04:26:16 UTC; windows
Archs: i386, x64
