SSM-class: An S4 class to represent a smooth supersaturated model
In SSM: Fit and Analyze Smooth Supersaturated Models

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An S4 class to represent a smooth supersaturated model

dimension: A number indicating the number of variables in the design.
design: A matrix with rows indicating the design point and columns indicating the variable.
design_size: A number indicating the number of design points.
response: A design_size length vector of responses.
theta: A vector containing the fitted model coefficients.
basis: A matrix with each row being the exponent vector of a polynomial term.
basis_size: A number indicating the number of basis terms used in the model. This may be different from nrow(basis) if terms are excluded.
include: A vector containing the row numbers of the basis polynomials used in the model. This is used when interactions or variables are being excluded from the model.
K: A semi-positive definite matrix that defines the smoothing criteria.
P: A matrix that defines the polynomial basis in terms of a monomial basis.
design_model_matrix: A matrix.
variances: A vector of length basis_size containing the term variances.
total_variance: A length one vector containing the total variance.
main_sobol: A dimension length vector containing the Sobol index for each variable.
main_ind: A logical matrix indicating whether each term is included in the main effect corresponding to the column.
total_sobol: A dimension length vector containing the Total sensitivity index for each variable.
total_ind: A logical matrix indicating whether each term is included in the Total sensitivity index corresponding to the column.
int_sobol: A vector containing the Sobol index for interactions.
int_factors: A list of length the same as int_sobol indicating which interaction corresponds with each entry in int_sobol.
total_int: A vector containing the Total interaction indices of all second order interactions.
total_int_factors: A matrix where each row indicates the variables associated with the corresponding interaction in total_int.
distance: A matrix containing the distances used for computing the covariance matrix of the GP metamodel error estimate.
distance_type: A character defining the distance type used for computing distance. Can be one of "distance", "line", "product", "area", "proddiff", or "smoothdiff".
type: A character, either "exp", "matern32", that selects the correlation function used for the GP metamodel error estimate.
covariance: A positive definite matrix. The covariance matrix of the GP metamodel error estimate prior to scaling by sigma.
residuals: A design_size length vector containing the Leave-One-Out errors of the model at each design point.
sigma: A number indicating the scaling factor for covariance.
r: A number indicating the length factor for the correlation function.
local_smoothness: A design_size length vector containing the model smoothness at each design point.
LOO_RMSE: A number. The Leave-One-Out root mean square error.
legendre: logical. Indicates whether the default Legendre polynomial basis is being used.
fail: logical. Indicates whether the model fit was successful.