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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=dgpsi to link to this page.
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