Package: onlineCOV 1.3
Jun Li
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>.
Authors:
onlineCOV_1.3.tar.gz
onlineCOV_1.3.tar.gz(r-4.5-noble)onlineCOV_1.3.tar.gz(r-4.4-noble)
onlineCOV_1.3.tgz(r-4.4-emscripten)onlineCOV_1.3.tgz(r-4.3-emscripten)
onlineCOV.pdf |onlineCOV.html✨
onlineCOV/json (API)
| # Install 'onlineCOV' in R: |
| install.packages('onlineCOV', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:aa8590e84a. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-linux-x86_64 | OK | Oct 31 2024 |
Exports:nuisance.eststopping.rule
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimate nuisance parameters in the stopping rule. | nuisance.est |
| Online change-point detection by the stopping rule. | stopping.rule |