Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations
Locate the next design point(s) for a (D)GP emulator or a bundle of (D...
Continue training a DGP emulator
Restore the serialized emulator
Sequential design of a (D)GP emulator or a bundle of (D)GP emulators
Deep Gaussian process emulator construction
dgpsi: Interface to 'dgpsi' for Deep and Linked Gaussian Process Emula...
Validation and diagnostic plots for a sequential design
Get the number of threads
Gaussian process emulator construction
'python' environment initialization
Linked (D)GP emulator construction
Locate the next design point for a (D)GP emulator or a bundle of (D)GP...
Calculate the predictive negative log-likelihood
Pack GP and DGP emulators into a bundle
Validation plots of a constructed GP, DGP, or linked (D)GP emulator
Prediction from GP, DGP, or linked (D)GP emulators
Static pruning of a DGP emulator
Load the stored emulator
Serialize the constructed emulator
Set Emulator ID
Reset number of imputations for a DGP emulator
Random seed generator
Set the number of threads
Add or remove the Vecchia approximation
Summary of a constructed GP, DGP, or linked (D)GP emulator
Trace plot for DGP hyperparameters
Unpack a bundle of (D)GP emulators
Update a GP or DGP emulator
Validate a constructed GP, DGP, or linked (D)GP emulator
Locate the next design point for a (D)GP emulator or a bundle of (D)GP...
Trim the sequence of hyperparameter estimates within a DGP emulator
Save the constructed emulator
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/>.
Useful links