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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.
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Package details |
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| Author | Sevvandi Kandanaarachchi [aut, cre] (<https://orcid.org/0000-0002-0337-0395>), Rob Hyndman [aut] (<https://orcid.org/0000-0002-2140-5352>), Chris Fraley [ctb] |
| Maintainer | Sevvandi Kandanaarachchi <sevvandik@gmail.com> |
| License | GPL-3 |
| Version | 0.1.4 |
| URL | https://sevvandi.github.io/lookout/ |
| Package repository | View on CRAN |
| Installation |
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