C1                      A covariance matrix
C2                      A covariance matrix
HS_empirical_bootstrap_test
                        Empirical bootstrap test for separability of
                        covariance structure using Hilbert-Schmidt
                        distance
HS_gaussian_bootstrap_test
                        Gaussian (parametric) bootstrap test for
                        separability of covariance structure using
                        Hilbert-Schmidt distance
SurfacesData            A data set of surfaces
clt_test                Test for separability of covariance operators
                        for Gaussian process.
covsep                  covsep: tests for determining if the covariance
                        structure of 2-dimensional data is separable
difference_fullcov      compute the difference between the full sample
                        covariance and its separable approximation
empirical_bootstrap_test
                        Projection-based empirical bootstrap test for
                        separability of covariance structure
gaussian_bootstrap_test
                        Projection-based Gaussian (parametric)
                        bootstrap test for separability of covariance
                        structure
generate_surface_data   Generate surface data
marginal_covariances    estimates marginal covariances (e.g. row and
                        column covariances) of bi-dimensional sample
projected_differences   Compute the projection of the rescaled
                        difference between the sample covariance and
                        its separable approximation onto the separable
                        eigenfunctions
renormalize_mtnorm      renormalize a matrix normal random matrix to
                        have iid entries
rmtnorm                 Generate a sample from a Matrix Gaussian
                        distribution
