Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations R package dgpsi version 2.6.0 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/>. Software 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 Deyu Ming <deyu.ming.16@ucl.ac.uk> Comprehensive R Archive Network (CRAN) Deyu Ming [aut, cre, cph], Daniel Williamson [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=dgpsi/LICENSE) 2025-10-15 application/tgz https://CRAN.R-project.org/package=dgpsi doi:10.32614/CRAN.package.dgpsi en-US