Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable

Documentation for package ‘covsep’ version 1.1.0

covsep-package	covsep: tests for determining if the covariance structure of 2-dimensional data is separable
C1	A covariance matrix
C2	A covariance matrix
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
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
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
SurfacesData	A data set of surfaces