Package: deepgp
Type: Package
Title: Deep Gaussian Processes using MCMC
Version: 1.1.0
Date: 2022-12-13
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.  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: 2022-12-15 01:44:50 UTC; anniesauer
Repository: CRAN
Date/Publication: 2022-12-15 08:40:09 UTC
Built: R 4.3.0; x86_64-apple-darwin20; 2023-07-11 22:19:02 UTC; unix
Archs: deepgp.so.dSYM
