onlineCOV: Online Change Point Detection in High-Dimensional Covariance Structure

Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. Moreover, it allows both spatial and temporal dependence in data. The dependence can be estimated by a data-driven procedure. The level of threshold in the stopping rule can be determined at a pre-selected average run length. More detail can be seen in Li, L. and Li, J. (2020) "Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks." <arXiv:1911.07762>.

Version: 1.3
Published: 2020-03-23
Author: Lingjun Li and Jun Li
Maintainer: Jun Li <jli49 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: onlineCOV results


Reference manual: onlineCOV.pdf
Package source: onlineCOV_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: onlineCOV_1.3.tgz, r-oldrel: onlineCOV_1.3.tgz


Please use the canonical form to link to this page.