Fit and Analyze Smooth Supersaturated Models
Generate all desired exponent vectors of a given degree.
Compute unscaled covariance matrix from a supplied distance matrix and...
Compute the unscaled covariance matrix.
Compute Total interaction indices and Sobol indices for higher order i...
Compute main effects
Compute the Leave-One-Out error at all design points.
Compute the Sobol index for a given interaction.
Compute Total interaction variance
Compute Total effects
Compute the concentrated likelihood of a covariance matrix.
Construct the design model matrix
Construct the K matrix for a given univariate basis.
Construct the K matrix for a given multivariate basis.
Construct the change of basis matrix from univariate monomials to Lege...
Construct the change of basis matrix from multivariate monomials to Le...
Construct matrix of exponent vectors.
Estimate the parameters of the metamodel error estimating GP.
Compute the SSM vector of parameters.
Fit a smooth supersaturated model
Compute entry of K matrix.
Identify main effect terms
Identify total effect terms
Plot the concentrated likelihood of an SSM.
Average the values in a vector between two cutoff points specified by ...
Compute the distance matrix of an SSM design.
Optimize concentrated likelihood.
Compute second partial derivative of a smooth supersaturated model at ...
Plot smooth supersaturated model main effects
Point prediction of smooth supersaturated models.
Plot the sensitivity indices of a smooth supersaturated model.
Summarise SSM class object
Compute the smoothness of an SSM at all design points.
An S4 class to represent a smooth supersaturated model
SSM: A package for fitting smooth supersaturated models (SSM).
Transform a design to [-1, 1]^d
Update an SSM object with the term variances and Sobol indices
Creates an S4 class "SSM" and defines functions for fitting smooth supersaturated models, a polynomial model with spline-like behaviour. Functions are defined for the computation of Sobol indices for sensitivity analysis and plotting the main effects using FANOVA methods. It also implements the estimation of the SSM metamodel error using a GP model with a variety of defined correlation functions.