A collection of some tests commonly used for identifying
        outliers.

Provides implementations of some of the most important outlier detection algorithms. 
    Includes a tutorial mode option that shows a description of each algorithm and provides 
    a step-by-step execution explanation of how it identifies outliers from the given data 
    with the specified input parameters. References include the works of Azzedine Boukerche, 
    Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) 
    <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) 
    <doi:10.1002/widm.19>.

Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'.

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.

Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.