Package: deepgp
Type: Package
Title: Sequential Design for Deep Gaussian Processes using MCMC
Version: 0.3.1
Date: 2021-11-23
Author: Annie Sauer <anniees@vt.edu>
Maintainer: Annie Sauer <anniees@vt.edu>
Depends: R (>= 3.6)
Description: Performs model fitting and sequential design for deep Gaussian
     processes following Sauer, Gramacy, and Higdon (2020) <arXiv:2012.08015>.  
     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.  Sequential design criteria 
     include integrated mean-squared error (IMSE), active learning Cohn (ALC), 
     and expected improvement (EI).  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,
        Rcpp, mvtnorm
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: akima, knitr
RoxygenNote: 7.1.1
Packaged: 2021-12-06 18:12:56 UTC; anniesauer
Repository: CRAN
Date/Publication: 2021-12-07 08:00:07 UTC
Built: R 4.0.2; x86_64-apple-darwin17.0; 2021-12-08 11:42:26 UTC; unix
Archs: deepgp.so.dSYM
