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 |
DOI: | 10.32614/CRAN.package.ldbod |
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 |
Reference manual: | ldbod.pdf |
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-devel (arm64): ldbod_0.1.2.tgz, r-release (arm64): ldbod_0.1.2.tgz, r-oldrel (arm64): ldbod_0.1.2.tgz, r-devel (x86_64): 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 |
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