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: 0.1.0
Imports: TDAstats, evd, RANN, ggplot2, tidyr
Suggests: knitr, rmarkdown
Published: 2021-02-12
Author: Sevvandi Kandanaarachchi ORCID iD [aut, cre], Rob Hyndman ORCID iD [aut]
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_0.1.0.tar.gz
Windows binaries: r-devel: lookout_0.1.0.zip, r-release: lookout_0.1.0.zip, r-oldrel: lookout_0.1.0.zip
macOS binaries: r-release (arm64): lookout_0.1.0.tgz, r-release (x86_64): lookout_0.1.0.tgz, r-oldrel: lookout_0.1.0.tgz

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