NPCovSepTest

This is a README file of the R package NPCovSepTest. In our paper, available here, we test the separability of the covariance matrix by permutation-based methods.

Installation of the package

To install our package, you may simply execute the following codes:

# install.packages("devtools")
devtools::install_github("inmybrain/NPCovSepTest", subdir = "NPCovSepTest") # don't forget to specify subdir!

If you come across a problem like this, please refer to this answer in that issue.

Or you can install the source file using the command line after downloading it from here;

R CMD INSTALL NPCovSepTest_2.0.tar.gz

A basic example of using the package

We give a toy example to test the main functions Min_PermTest, Max_PermTest, and Twost_PermTest. First, we generate (20) samples from the multivariate Gaussian distribution with a separable covariance matrix.

# install.packages("mvtnorm")
library("NPCovSepTest")
U <- matrix(c(1,0.5,0.5,1), nrow = 2)
V <- diag(rep(1,3))
set.seed(6)
Y <- mvtnorm::rmvnorm(20, sigma = U %x% V)

Min_PermTest(Y, 2, 3)
Max_PermTest(Y, 2, 3)
Twost_PermTest(Y, 2, 3)

All results indicate there is no strong evidence to reject the null, i.e. separability of the covariance matrix.

Notes

Citation

To cite this package, please use this bibtex format:

@article{Park:2019, 
  title = {Permutation based testing on covariance separability},
  DOI = {10.1007 / s00180 - 018 - 0839 - 2}, 
  journal = {Computational Statistics}, 
  author = {Park, Seongoh and Lim, Johan and Wang, Xinlei and Lee, Sanghan}, 
  year = {2019}, 
  volume = {34},
    number = {2},
    pages = {865–883}
}

Issues

We are happy to troubleshoot any issue with the package;

• please contact to the maintainer by [email protected], or

• please open an issue in the github repository.