Package: deepICMGP
Title: Deep Intrinsic Coregionalization Gaussian Processes for Multiple
        Outputs
Version: 0.1.1
Authors@R: 
    c(person(given="Chun-Yi", family="Chang", role=c("aut", "cre"), email="chang103@msu.edu"),
      person(given="Chih-Li", family="Sung", role="aut", email="sungchih@msu.edu"),
      person(given = c("Annie", "S."), family = "Booth", role = "ctb",
       comment = "Author of deepgp code adapted in this package")
	)
Author: Chun-Yi Chang [aut, cre],
  Chih-Li Sung [aut],
  Annie S. Booth [ctb] (Author of deepgp code adapted in this package)
Maintainer: Chun-Yi Chang <chang103@msu.edu>
Description: Fits two-layer deep intrinsic coregionalization Gaussian process
    surrogate models for multiple outputs observed at shared input locations.
    Uses Markov chain Monte Carlo sampling for kernel parameters and latent
    coordinates, with plug-in coregionalization estimates. Provides burn-in
    removal and thinning, posterior predictive means and within-location output
    covariances, and sequential experimental design using an active learning 
    criterion. The method is described by Chang and Sung (2026) 
    <doi:10.1615/Int.J.UncertaintyQuantification.2026062570>.
    Adapts code from the 'deepgp' package, with underlying deep Gaussian process
    methodology described by Sauer, Gramacy, and Higdon (2023)
    <doi:10.1080/00401706.2021.2008505>.
License: LGPL
Encoding: UTF-8
Imports: lhs, parallel, stats
Config/roxygen2/version: 8.0.0
NeedsCompilation: yes
Packaged: 2026-10-21 18:59:35 UTC; chang103
Repository: CRAN
Date/Publication: 2026-11-03 15:30:02 UTC
