Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.

This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. Additionally, it includes a function to recommend the optimal tuning parameter, alpha, which controls the robustness of the MDPDE. The methods implemented in this package are based on Song et al. (2017) <doi:10.1016/j.csda.2016.08.005>.

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.

Provides robust statistical methods for analyzing numeric data,
 including robust estimation of location and scale using Huber M-estimators
 and a robust two-sample t-test. Methods are based on Huber (1981,
 ISBN:0471418056) "Robust Statistics" and Smyth (2004)
 <doi:10.2202/1544-6115.1027>.