dgpsi
2.6.0Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations
Overview
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/.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-1013 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Documentation
- Examples that run
- 0%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 13%
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Dependencies
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Code & Tests
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7 releases. Pick two to compare their code metrics. R releases are shown for context.
Package metadata
- First published
- 2022-09-29
- Total releases
- 7 / 4 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.0
- Download size
- 703 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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