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++.

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}.

Implementations for several robust procedures that allow for (online)
        extraction of the signal of univariate or multivariate time series by
        applying robust regression techniques to a moving time window are provided.
        Included are univariate filtering procedures based on repeated-median 
        regression as well as hybrid and trimmed filters derived from it; 
        see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive 
        online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> 
        and the slope comparing adaptive repeated median by Borowski and Fried (2013) 
        <doi:10.1007/s11222-013-9391-7> choose the width of the moving time 
        window adaptively. Multivariate versions are also provided; see  
        Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate 
        online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393>  
        for a multivariate slope comparing adaptive repeated median. Furthermore, 
        a repeated-median based filter with automatic outlier replacement and 
        shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.