Package: deepgp
Type: Package
Title: Sequential Design for Deep Gaussian Processes using MCMC
Version: 0.2.1
Date: 2021-07-15
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.  Sequential design criteria include 
     integrated mean-squared error (IMSE), active learning Cohn (ALC), and 
     expected improvement (EI).  Covariance structure is based on inverse 
     exponentiated squared euclidean distance.  Applicable to noisy and 
     deterministic functions.  Incorporates SNOW parallelization and utilizes 
     C under the hood.
License: LGPL
Encoding: UTF-8
NeedsCompilation: yes
Imports: grDevices, graphics, stats, doParallel, foreach, parallel
Suggests: akima, knitr
RoxygenNote: 7.1.1
Packaged: 2021-07-15 15:20:51 UTC; anniesauer
Repository: CRAN
Date/Publication: 2021-07-15 15:40:07 UTC
