GPM: Gaussian Process Modeling of Multi-Response Datasets

Provides a general and efficient tool for fitting a response surface to datasets via Gaussian processes. The dataset can have multiple responses and 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: 1.0.2
Depends: R (≥ 3.2.5), stats (≥ 3.2.5)
Imports: lhs (≥ 0.14), randtoolbox (≥ 1.17), lattice (≥ 0.20-34), grDevices, graphics
Published: 2018-07-23
Author: Ramin Bostanabad, Wei Chen (IDEAL)
Maintainer: Ramin Bostanabad <bostanabad at u.northwestern.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: GPM results

Downloads:

Reference manual: GPM.pdf
Package source: GPM_1.0.2.tar.gz
Windows binaries: r-devel: GPM_1.0.2.zip, r-release: GPM_1.0.2.zip, r-oldrel: GPM_1.0.2.zip
OS X binaries: r-release: GPM_1.0.2.tgz, r-oldrel: GPM_1.0.2.tgz
Old sources: GPM archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GPM to link to this page.