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.

Getting started

Package details

AuthorRamin Bostanabad, Tucker Kearney, Siyo Tao, Daniel Apley, and Wei Chen (IDEAL)
MaintainerRamin Bostanabad <[email protected]>
LicenseGPL-2
Version3.0.1
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("GPM")

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GPM documentation built on May 2, 2019, 12:36 a.m.