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 |