GPM v3.0.0

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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.

Functions in GPM

Name Description
Fit The Fitting Function of GPM Package
Predict The Prediction Function of GPM Package
Auxil An auxiliary function used in calculating the negative log-likelehood and its gradient
MatrixAlgebra A Set of Functions for Doing Some Calculations on Matrices in GPM Package
NLogL_G The Function for calculating the gradient of Negative Log-Likelehood in GPM Package
NLogL The Function for calculating the Negative Log-Likelehood in GPM Package
Draw The Plotting Function of GPM Package
CorrMat Two Functions for Constructing the Correlation Matrix in GPM Package
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Details

Type Package
Date 2019-01-11
License GPL-2
LazyData FALSE
Encoding UTF-8
LinkingTo Rcpp, RcppArmadillo
NeedsCompilation yes
Packaged 2019-01-11 17:00:32 UTC; Ramin
Repository CRAN
Date/Publication 2019-01-11 17:20:07 UTC

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