Interface to the 'python' package 'dgpsi' for Gaussian process, deep Gaussian process, and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and Ming & Williamson (2023) <doi:10.48550/arXiv.2306.01212>. To get started with the package, see <https://mingdeyu.github.io/dgpsi-R/>.
| Version: | 2.4.0 |
| Depends: | R (≥ 4.0) |
| Imports: | reticulate (≥ 1.26), benchmarkme (≥ 1.0.8), utils, ggplot2, ggforce, reshape2, patchwork, lhs, methods, stats, bitops, clhs, dplyr, uuid |
| Suggests: | knitr, rmarkdown, MASS, R.utils, spelling |
| Published: | 2024-01-14 |
| DOI: | 10.32614/CRAN.package.dgpsi |
| Author: | Deyu Ming [aut, cre, cph], Daniel Williamson [aut] |
| Maintainer: | Deyu Ming <deyu.ming.16 at ucl.ac.uk> |
| BugReports: | https://github.com/mingdeyu/dgpsi-R/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/mingdeyu/dgpsi-R, https://mingdeyu.github.io/dgpsi-R/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | dgpsi citation info |
| Materials: | README NEWS |
| CRAN checks: | dgpsi results |
| Reference manual: | dgpsi.pdf |
| Vignettes: |
A Quick Guide to dgpsi Linked (D)GP Emulation DGP Emulation with the Heteroskedastic Gaussian Likelihood Sequential Design I Sequential Design II |
| Package source: | dgpsi_2.4.0.tar.gz |
| Windows binaries: | r-devel: dgpsi_2.4.0.zip, r-release: dgpsi_2.4.0.zip, r-oldrel: dgpsi_2.4.0.zip |
| macOS binaries: | r-release (arm64): dgpsi_2.4.0.tgz, r-oldrel (arm64): dgpsi_2.4.0.tgz, r-release (x86_64): dgpsi_2.4.0.tgz, r-oldrel (x86_64): dgpsi_2.4.0.tgz |
| Old sources: | dgpsi archive |
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