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: | 2.0.0 |
| Imports: | evd,ggplot2,RANN,robustbase, stats,TDAstats,tidyr |
| Suggests: | knitr,rmarkdown |
| Published: | 2026-01-19 |
| DOI: | 10.32614/CRAN.package.lookout |
| Author: | Sevvandi Kandanaarachchi [aut, cre], Rob Hyndman [aut], Chris Fraley [ctb] |
| Maintainer: | Sevvandi Kandanaarachchi <sevvandik at gmail.com> |
| BugReports: | https://github.com/sevvandi/lookout/issues |
| License: | GPL-3 |
| URL: | https://sevvandi.github.io/lookout/,https://github.com/sevvandi/lookout |
| NeedsCompilation: | no |
| Materials: | README,NEWS |
| In views: | AnomalyDetection |
| CRAN checks: | lookout results |
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