Kriging Methods for Computer Experiments
Estimation, validation and prediction of kriging models. Important functions : km, print.km, plot.km, predict.km.
Penalty function
Penalty function derivative
2D test function
Consistency test between the column names of two matrices
Get coefficients values
Auxiliary variables for kriging
Class of tensor-product spatial covariances with isotropic range
Class "covKernel"
Cross covariance matrix
Covariance matrix
Covariance matrix derivatives
Boundaries for covariance parameters
Class "covScaling"
Spatial covariance - Class constructor
Class of tensor-product spatial covariances
Class "covUser"
Spatial covariance - Derivatives
Auxiliary function
Multiple fold cross validation for a km object
Trend model formula operation
3D test function
6D test function
Get the input variables names
Get the kernel name
Fit and/or create kriging models
Kriging models class
Fitting Kriging Models
Leave-one-out for a km object
Leave-one-out least square criterion of a km object
Leave-one-out least square criterion - Analytical gradient
log-likelihood of a km object
Concentrated log-likelihood of a km object
Concentrated log-Likelihood of a km object - Analytical gradient
Get the spatial dimension
Get the nugget flag
Get or set the nugget value
Diagnostic plot for the validation of a km object
Predict values and confidence intervals at newdata for a km object
Scaling function
Scaling 1-dimensional function
Gradient of the dimensional Scaling function
Print values of a km object
Simulate GP values at any given set of points for a km object
Trend derivatives
Trend model matrix operation
Update of a kriging model
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