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.

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>
License: GPL-2
NeedsCompilation: yes
CRAN checks: GPM results


Reference manual: GPM.pdf


Package source: GPM_3.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GPM_3.0.1.tgz, r-oldrel (arm64): GPM_3.0.1.tgz, r-release (x86_64): GPM_3.0.1.tgz, r-oldrel (x86_64): GPM_3.0.1.tgz
Old sources: GPM archive

Reverse dependencies:

Reverse enhances: joinet


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