Package: covsep 1.1.1

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

Functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston et al. (2017) <doi:10.1214/16-AOS1495> <doi:10.48550/arXiv.1505.02023>.

Authors:Shahin Tavakoli [aut, cre], Davide Pigoli [ctb], John Aston [ctb]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
covsep/json (API)

# Install 'covsep' in R:

install.packages('covsep', repos = c('https://stavakol.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stavakol/covsep/issues

Datasets:

C1 - A covariance matrix
C2 - A covariance matrix
SurfacesData - A data set of surfaces

On CRAN:

2.23 score 17 scripts 375 downloads 10 exports 1 dependencies

Last updated from:3c267bcc18. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	152
source / vignettes	OK	133
linux-release	OK	125
macos-release	OK	121
macos-oldrel	OK	126
windows-devel	OK	77
windows-release	OK	66
windows-oldrel	OK	61
wasm-release	OK	95

Exports:clt_test empirical_bootstrap_test gaussian_bootstrap_test generate_surface_data HS_empirical_bootstrap_test HS_gaussian_bootstrap_test marginal_covariances projected_differences renormalize_mtnorm rmtnorm

Dependencies:mvtnorm

Citation

Development and contributors

Readme and manuals

Help Manual

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