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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3 |
rolling linux/jammy R-4.5 | onlineCOV_1.3.tar.gz |
27.2 KiB |
1.3 |
rolling linux/noble R-4.5 | onlineCOV_1.3.tar.gz |
27.2 KiB |
1.3 |
rolling source/ R- | onlineCOV_1.3.tar.gz |
5.6 KiB |
1.3 |
latest linux/jammy R-4.5 | onlineCOV_1.3.tar.gz |
27.2 KiB |
1.3 |
latest linux/noble R-4.5 | onlineCOV_1.3.tar.gz |
27.2 KiB |
1.3 |
latest source/ R- | onlineCOV_1.3.tar.gz |
5.6 KiB |
1.3 |
2026-04-26 source/ R- | onlineCOV_1.3.tar.gz |
5.6 KiB |
1.3 |
2026-04-23 source/ R- | onlineCOV_1.3.tar.gz |
5.6 KiB |
1.3 |
2026-04-09 windows/windows R-4.5 | onlineCOV_1.3.zip |
33.8 KiB |
1.3 |
2025-04-20 source/ R- | onlineCOV_1.3.tar.gz |
5.6 KiB |