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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: | 2.5.0 |
| Depends: | R (≥ 4.0) |
| Imports: | reticulate (≥ 1.26), benchmarkme (≥ 1.0.8), utils, ggplot2, ggforce, reshape2, patchwork, lhs, methods, stats, clhs, dplyr, uuid, tidyr, rlang, lifecycle, magrittr, visNetwork, parallel, kableExtra |
| Suggests: | knitr, rmarkdown, MASS, R.utils, spelling |
| Published: | 2024-12-14 |
| 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 (source, R code) |
| Package source: | dgpsi_2.5.0.tar.gz |
| Windows binaries: | r-devel: dgpsi_2.5.0.zip, r-release: dgpsi_2.5.0.zip, r-oldrel: dgpsi_2.5.0.zip |
| macOS binaries: | r-release (arm64): dgpsi_2.5.0.tgz, r-oldrel (arm64): dgpsi_2.5.0.tgz, r-release (x86_64): dgpsi_2.5.0.tgz, r-oldrel (x86_64): dgpsi_2.5.0.tgz |
| Old sources: | dgpsi archive |
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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.
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