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.
Version: 3.0.1 Depends: R (≥ 3.5), stats (≥ 3.5) Imports: Rcpp (≥ 0.12.19), lhs (≥ 0.14), randtoolbox (≥ 1.17), lattice (≥ 0.20-34), pracma (≥ 2.1.8), foreach (≥ 1.4.4), doParallel (≥ 1.0.14), parallel (≥ 3.5), iterators (≥ 1.0.10) LinkingTo: Rcpp, RcppArmadillo Suggests: RcppArmadillo Published: 2019-03-21 DOI: 10.32614/CRAN.package.GPM Author: Ramin Bostanabad, Tucker Kearney, Siyo Tao, Daniel Apley, and Wei Chen (IDEAL) Maintainer: Ramin Bostanabad <bostanabad at u.northwestern.edu> License: GPL-2 NeedsCompilation: yes CRAN checks: GPM results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=GPM to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4