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/>.">

dgpsi: Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations (original) (raw)

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
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

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