Package: CGP 2.1-1
CGP: Composite Gaussian Process Models
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary. Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
Authors:
CGP_2.1-1.tar.gz
CGP_2.1-1.zip(r-4.7)CGP_2.1-1.zip(r-4.6)CGP_2.1-1.zip(r-4.5)
CGP_2.1-1.tgz(r-4.6-any)CGP_2.1-1.tgz(r-4.5-any)
CGP_2.1-1.tar.gz(r-4.7-any)CGP_2.1-1.tar.gz(r-4.6-any)
CGP_2.1-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CGP/json (API)
| # Install 'CGP' in R: |
| install.packages('CGP', repos = c('https://shanbatr.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:05bcbe3b54. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 98 | ||
| source / vignettes | OK | 145 | ||
| linux-release-x86_64 | OK | 87 | ||
| macos-release-arm64 | OK | 95 | ||
| macos-oldrel-arm64 | OK | 75 | ||
| windows-devel | OK | 71 | ||
| windows-release | OK | 57 | ||
| windows-oldrel | OK | 67 | ||
| wasm-release | OK | 74 |
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| The composite Gaussian process model package | CGP-package |
| Fit composite Gaussian process models | CGP |
| Jackknife (leave-one-out) actual by predicted diagnostic plot | plotCGP |
| Predict from the composite Gaussian process model | predict.CGP |
| CGP model summary information | print.CGP |
| CGP model summary information | summary.CGP |
