The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.

Robust estimators for generalized ratio model (Wada, Sakashita and Tsubaki, 2021)<doi:10.17713/ajs.v50i1.994> and linear regression model by the IRLS(iterative reweighted least squares) algorithm are contained.

Three-step regression and inference for cellwise and casewise contamination.

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

Robust methods for estimating the parameters of multivariate Gaussian linear models.

A framework for estimating ensembles of parametric survival models
    with different parametric families. The RoBSA framework uses Bayesian 
    model-averaging to combine the competing parametric survival models into 
    a model ensemble, weights the posterior parameter distributions based on 
    posterior model probabilities and uses Bayes factors to test for the 
    presence or absence of the individual predictors or preference for a 
    parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>).
    The user can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    fit diagnostics, and prior distribution calibration.

Estimates robust location and scale parameters using
    platform-specific Single Instruction, Multiple Data (SIMD) vectorization
    and Intel Threading Building Blocks (TBB) for parallel processing.
    Implements a novel variance-weighted ensemble estimator that adaptively
    combines all available statistics. Methods include logistic
    M-estimators, the estimators of Rousseeuw and Croux (1993), the Gini
    mean difference, the scaled Median Absolute Deviation (MAD), the scaled
    Interquartile Range (IQR), and unbiased standard deviations. Achieves
    substantial speedups over existing implementations through an 'Rcpp'
    backend with fused single-buffer algorithms that halve memory traffic
    for MAD and M-scale estimation, and a unified dispatcher that
    automatically selects the optimal estimator based on sample size.