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BlockCov (version 0.1.1)

Estimation of Large Block Covariance Matrices

Description

Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Docks et al. (2019) .

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Install

install.packages('BlockCov')

Monthly Downloads

150

Version

0.1.1

License

GPL (>= 2)

Maintainer

Marie Perrot-Dockc3<a8>s

Last Published

April 13th, 2019

Functions in BlockCov (0.1.1)

Sigma_estimation

This function computes an estimator of the covariance matrix and the square root of its inverse and permutes its rows and columns if it is necessary to make the block structure appear.

This function generates a block structured symmetric positive definite matrix to test the BlockCov methodology.

BlockCov package

This function fits to a numerical vector sorted in the non decreasing order two simple linear regressions and returns the index corresponding to the estimated change between the two regression models.