lookout: Leave One Out Kernel Density Estimates for Outlier Detection
Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.
| Version: | 0.1.4 |
| Imports: | TDAstats, evd, RANN, ggplot2, tidyr |
| Suggests: | knitr, rmarkdown |
| Published: | 2022-10-14 |
| DOI: | 10.32614/CRAN.package.lookout |
| Author: | Sevvandi Kandanaarachchi
 [aut, cre],
Rob Hyndman [aut],
Chris Fraley [ctb] |
| Maintainer: | Sevvandi Kandanaarachchi <sevvandik at gmail.com> |
| License: | GPL-3 |
| URL: | https://sevvandi.github.io/lookout/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | lookout results |
Documentation:
| Reference manual: | lookout.pdf |
Downloads:
| Package source: | lookout_0.1.4.tar.gz |
| Windows binaries: | r-devel: lookout_0.1.4.zip, r-release: lookout_0.1.4.zip, r-oldrel: lookout_0.1.4.zip |
| macOS binaries: | r-release (arm64): lookout_0.1.4.tgz, r-oldrel (arm64): lookout_0.1.4.tgz, r-release (x86_64): lookout_0.1.4.tgz, r-oldrel (x86_64): lookout_0.1.4.tgz |
| Old sources: | lookout archive |
Reverse dependencies:
| Reverse imports: | oddnet |
Linking:
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