ldbod: Local Density-Based Outlier Detection

Flexible procedures to compute local density-based outlier scores for ranking outliers. Both exact and approximate nearest neighbor search can be implemented, while also accommodating multiple neighborhood sizes and four different local density-based methods. It allows for referencing a random subsample of the input data or a user specified reference data set to compute outlier scores against, so both unsupervised and semi-supervised outlier detection can be implemented.

Version:	0.1.2
Depends:	R (≥ 3.2.0)
Imports:	stats, RANN, mnormt
Published:	2017-05-26
Author:	Kristopher Williams
Maintainer:	Kristopher Williams <kristopher.williams83 at gmail.com>
License:	GPL-3
URL:	https://github.com/kwilliams83/ldbod
NeedsCompilation:	no
Materials:	README
CRAN checks:	ldbod results

Documentation:

Reference manual:

ldbod.pdf

Downloads:

Package source:	ldbod_0.1.2.tar.gz
Windows binaries:	r-devel: ldbod_0.1.2.zip, r-release: ldbod_0.1.2.zip, r-oldrel: ldbod_0.1.2.zip
macOS binaries:	r-release (arm64): ldbod_0.1.2.tgz, r-oldrel (arm64): ldbod_0.1.2.tgz, r-release (x86_64): ldbod_0.1.2.tgz, r-oldrel (x86_64): ldbod_0.1.2.tgz
Old sources:	ldbod archive

Linking:

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