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>
License: GPL-2
NeedsCompilation: no
CRAN checks: GPM results


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


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