CRAN
GPM 3.0.1
Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets
Released Mar 21, 2019 by Ramin Bostanabad
Dependencies
randtoolbox 1.17.1 lhs 1.0.1 Rcpp iterators 1.0.10 lattice 0.20-38 RcppArmadillo 0.9.300.2.0 foreach 1.4.4 pracma 2.2.2 doParallel 1.0.14
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.