Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <doi:10.48550/arXiv.2107.00975>.

Implements a robust multivariate control-chart methodology for
    batch-based industrial processes with multiple correlated variables using the
    Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework.
    A robust compromise covariance matrix is constructed from Phase I batches with the
    Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is
    applied for anomaly detection in Phase II. The package includes functions to simulate
    clean and contaminated batches, to compute both robust and classical Hotelling T²
    control charts, to visualize results via robust biplots, and to launch an interactive
    'shiny' dashboard. An internal dataset (pharma_data) is provided for reproducibility.
    See Lavit, Escoufier, Sabatier and Traissac (1994) <doi:10.1016/0167-9473(94)90134-1>
    for the original STATIS methodology, and Rousseeuw and Van Driessen (1999)
    <doi:10.1080/00401706.1999.10485670> for the MCD estimator.

Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary'
  Functionality for Robust Statistics.  Hence methods which are not well established,
  often related to methods in package 'robustbase'.  Amazingly, 'BACON()', originally by
  Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>
  has become established in places.  The "barrow wheel" `rbwheel()` is from
  Stahel and Mächler (2009) <doi:10.1111/j.1467-9868.2009.00706.x>.