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.


Reference manual

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3.0.0 by Ramin Bostanabad, 10 days ago

Browse source code at

Authors: Ramin Bostanabad , Tucker Kearney , Siyo Tao , Daniel Apley , and Wei Chen (IDEAL)

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, lhs, randtoolbox, lattice, pracma, foreach, doParallel, parallel, iterators

Depends on stats

Suggests RcppArmadillo

Linking to Rcpp, RcppArmadillo

See at CRAN