Package: deepgp
Type: Package
Title: Sequential Design for Deep Gaussian Processes using MCMC
Version: 0.1.0
Date: 2020-10-8
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 using MCMC and elliptical slice sampling.  Models extend up to 
    three layers deep; a one layer model is equivalent to typical Gaussian 
    process regression.  Sequential design criteria include integrated mean 
    square prediction error (IMSPE), 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.  Manuscript 
    forthcoming; see Damianou and Lawrence (2013) <arXiv:1211.0358> for deep 
    Gaussian process models and Murray, Adams, and MacKay (2010) <arXiv:1001.0175> 
    for elliptical slice sampling.
License: LGPL
Encoding: UTF-8
NeedsCompilation: yes
Imports: grDevices, graphics, stats, doParallel, foreach, parallel
Suggests: akima, knitr
RoxygenNote: 7.1.1
Packaged: 2020-10-20 18:38:46 UTC; anniesauer
Repository: CRAN
Date/Publication: 2020-10-29 10:20:08 UTC
