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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.

Versions across snapshots

VersionRepositoryFileSize
0.1.2 rolling linux/jammy R-4.5 ldbod_0.1.2.tar.gz 42.2 KiB
0.1.2 rolling linux/noble R-4.5 ldbod_0.1.2.tar.gz 42.0 KiB
0.1.2 rolling source/ R- ldbod_0.1.2.tar.gz 9.2 KiB
0.1.2 latest linux/jammy R-4.5 ldbod_0.1.2.tar.gz 42.2 KiB
0.1.2 latest linux/noble R-4.5 ldbod_0.1.2.tar.gz 42.0 KiB
0.1.2 latest source/ R- ldbod_0.1.2.tar.gz 9.2 KiB
0.1.2 2026-04-26 source/ R- ldbod_0.1.2.tar.gz 9.2 KiB
0.1.2 2026-04-23 source/ R- ldbod_0.1.2.tar.gz 9.2 KiB
0.1.2 2026-04-09 windows/windows R-4.5 ldbod_0.1.2.zip 44.5 KiB
0.1.2 2025-04-20 source/ R- ldbod_0.1.2.tar.gz 9.2 KiB

Dependencies (latest)

Imports