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:	0.1.2
Imports:	TDAstats, evd, RANN, ggplot2, tidyr
Suggests:	knitr, rmarkdown
Published:	2022-08-26
Author:	Sevvandi Kandanaarachchi [aut, cre], Rob Hyndman [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_0.1.2.tar.gz
Windows binaries:	r-devel: lookout_0.1.2.zip, r-release: lookout_0.1.2.zip, r-oldrel: lookout_0.1.2.zip
macOS binaries:	r-release (arm64): lookout_0.1.2.tgz, r-oldrel (arm64): lookout_0.1.2.tgz, r-release (x86_64): lookout_0.1.2.tgz, r-oldrel (x86_64): lookout_0.1.2.tgz
Old sources:	lookout archive

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