Sequential outlier identification for Gaussian mixture models using
    the distribution of Mahalanobis distances. The optimal number
    of outliers is chosen based on the dissimilarity between the theoretical and
    observed distributions of the scaled squared sample Mahalanobis distances.
    Also includes an extension for Gaussian linear cluster-weighted models using
    the distribution of studentized residuals. 
    Doherty, McNicholas, and White (2025) <doi:10.48550/arXiv.2505.11668>.

A collection of some tests commonly used for identifying
        outliers.

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.

Algorithms to detect high-dimensional outliers. The minimum diagonal product of Ro, Zou, Wang and Yin (2015) <doi:10.1093/biomet/asv021>, the algorithm of Wilkinson (2018) <doi:10.1109/TVCG.2017.2744685>, and the distances of distances of Lee and Jeon (2025) <doi:10.48550/arXiv.2511.02199>.

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 FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'.