outlierspinner: Geometric Multivariate Outlier Detection via Random Directional
Probing
Provides tools for multivariate outlier detection based on
geometric properties of multivariate data using random directional
projections. Observation-level outlier scores are computed by jointly
probing radial magnitude and angular alignment through repeated
projections onto random directions, with optional robust centering and
covariance adjustment. In addition to global outlier scoring, the method
produces dimension-level contribution measures to support interpretation
of detected anomalies. Visualization utilities are included to summarize
directional contributions for extreme observations.
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