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.