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

Documentation:

Reference manual:

lookout.html , lookout.pdf

Downloads:

Package source:	lookout_2.0.0.tar.gz
Windows binaries:	r-devel: lookout_2.0.0.zip, r-release: lookout_0.1.4.zip, r-oldrel: lookout_0.1.4.zip
macOS binaries:	r-release (arm64): lookout_2.0.0.tgz, r-oldrel (arm64): lookout_2.0.0.tgz, r-release (x86_64): lookout_2.0.0.tgz, r-oldrel (x86_64): lookout_2.0.0.tgz
Old sources:	lookout archive

Reverse dependencies:

Reverse imports:

oddnet

Linking:

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