The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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>.
| Version: | 0.1.1 |
| Imports: | lhs, parallel, stats |
| Published: | 2026-11-03 |
| DOI: | 10.32614/CRAN.package.deepICMGP |
| 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 at msu.edu> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| NeedsCompilation: | yes |
| Citation: | deepICMGP citation info |
| CRAN checks: | deepICMGP results |
| Reference manual: | deepICMGP.html , deepICMGP.pdf |
| Package source: | deepICMGP_0.1.1.tar.gz |
| Windows binaries: | r-devel: deepICMGP_0.1.1.zip, r-release: deepICMGP_0.1.1.zip, r-oldrel: deepICMGP_0.1.1.zip |
| macOS binaries: | r-release (arm64): deepICMGP_0.1.1.tgz, r-oldrel (arm64): deepICMGP_0.1.1.tgz, r-release (x86_64): deepICMGP_0.1.1.tgz, r-oldrel (x86_64): deepICMGP_0.1.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=deepICMGP to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.