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: 2026-11-04 13:16:42 UTC; root
Repository: https://jennychang32.r-universe.dev
Date/Publication: 2026-11-03 15:30:02 UTC
RemoteUrl: https://github.com/cran/deepICMGP
RemoteRef: HEAD
RemoteSha: f355b8ad21cc51dc5e39ce3b9404c4e42fce3737