Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.

Gene-environment (G×E) interactions have important implications to elucidate the 
    etiology of complex diseases beyond the main genetic and environmental effects. 
    Outliers and data contamination in disease phenotypes of G×E studies have been commonly 
    encountered, leading to the development of a broad spectrum of robust penalization methods. 
    Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing 
    studies. We develop a robust Bayesian variable selection method for G×E interaction 
    studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and 
    outliers in the response variable while conducting variable selection by accounting for 
    structural sparsity. In particular, the spike-and-slab priors have been imposed on both 
    individual and group levels to identify important main and interaction effects. An efficient 
    Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo 
    algorithms of the proposed and alternative methods are efficiently implemented in C++.

Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.

Optimally robust estimation for extreme value distributions using S4 classes and
            methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
            'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and
            Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and
            \doi{10.1007/s00184-011-0366-4}.