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onlineCOV  

Online Change Point Detection in High-Dimensional Covariance Structure
View on CRAN: Click here


Download and install onlineCOV package within the R console
Install from CRAN:
install.packages("onlineCOV")

Install from Github:
library("remotes")
install_github("cran/onlineCOV")

Install by package version:
library("remotes")
install_version("onlineCOV", "1.3")



Attach the package and use:
library("onlineCOV")
Maintained by
Jun Li
[Scholar Profile | Author Map]
First Published: 2020-03-23
Latest Update: 2020-03-23
Description:
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." .
How to cite:
Jun Li (2020). onlineCOV: Online Change Point Detection in High-Dimensional Covariance Structure. R package version 1.3, https://cran.r-project.org/web/packages/onlineCOV. Accessed 17 Apr. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited onlineCOV R package
View onlineCOV citation profile
Other R packages that onlineCOV depends, imports, suggests or enhances
Complete documentation for onlineCOV
Functions, R codes and Examples using the onlineCOV R package
Some associated functions: nuisance.est . stopping.rule . 
Some associated R codes: B.mat.R . M.est.R . cor.est.R . nuisance.est.R . stat.test.R . stopping.rule.R .  Full onlineCOV package functions and examples
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