lookout: Leave One Out Kernel Density Estimates for Outlier Detection (original) (raw)
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 |
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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 |
License: | GPL-3 |
URL: | https://sevvandi.github.io/lookout/ |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | lookout results |
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