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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." <doi:10.48550/arXiv.1911.07762>.
| Version: | 1.3 |
| Published: | 2020-03-23 |
| Author: | Lingjun Li and Jun Li |
| Maintainer: | Jun Li <jli49 at kent.edu> |
| 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: onlineCOV_1.3.zip, r-release: onlineCOV_1.3.zip, r-oldrel: onlineCOV_1.3.zip |
| macOS binaries: | r-release (arm64): onlineCOV_1.3.tgz, r-oldrel (arm64): onlineCOV_1.3.tgz, r-release (x86_64): onlineCOV_1.3.tgz, r-oldrel (x86_64): onlineCOV_1.3.tgz |
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