Package: GPM 3.0.1
Ramin Bostanabad
GPM: Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets
Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
Authors:
GPM_3.0.1.tar.gz
GPM_3.0.1.tar.gz(r-4.5-noble)GPM_3.0.1.tar.gz(r-4.4-noble)
GPM_3.0.1.tgz(r-4.4-emscripten)GPM_3.0.1.tgz(r-4.3-emscripten)
GPM.pdf |GPM.html✨
GPM/json (API)
| # Install 'GPM' in R: |
| install.packages('GPM', repos = c('https://cran.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 6 years agofrom:0d1b5a1f18. Checks:OK: 1 NOTE: 1. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 05 2024 |
| R-4.5-linux-x86_64 | NOTE | Dec 05 2024 |
Exports:AuxilCorrMat_SymCorrMat_VecCppSolveDrawEigenFitLowerCholNLogLNLogL_GPredict
Dependencies:codetoolsdoParallelforeachiteratorslatticelhspracmarandtoolboxRcppRcppArmadillorngWELL
