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Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) <doi:10.48550/arXiv.2210.07407>.
Version: | 0.1.1 |
Imports: | dplyr, fable, fabletools, igraph, lookout, pcaPP, rlang, tibble, tidyr, tsibble, utils |
Suggests: | DDoutlier, feasts, knitr, rmarkdown, rTensor, urca |
Published: | 2024-02-11 |
DOI: | 10.32614/CRAN.package.oddnet |
Author: | Sevvandi Kandanaarachchi
|
Maintainer: | Sevvandi Kandanaarachchi <sevvandik at gmail.com> |
License: | GPL (≥ 3) |
URL: | https://sevvandi.github.io/oddnet/ |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | oddnet results |
Reference manual: | oddnet.pdf |
Vignettes: |
oddnet |
Package source: | oddnet_0.1.1.tar.gz |
Windows binaries: | r-devel: oddnet_0.1.1.zip, r-release: oddnet_0.1.1.zip, r-oldrel: oddnet_0.1.1.zip |
macOS binaries: | r-release (arm64): oddnet_0.1.1.tgz, r-oldrel (arm64): oddnet_0.1.1.tgz, r-release (x86_64): oddnet_0.1.1.tgz, r-oldrel (x86_64): oddnet_0.1.1.tgz |
Old sources: | oddnet archive |
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These binaries (installable software) and packages are in development.
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