distance.cor            Measure an impact of the covariates on the
                        response using the distance correlation This
                        function evaluates the distance correlation
                        between the response 'y' and each column in the
                        design matrix 'x' over subsamples in
                        'subsamples'.
factor.model.design     Generate factor model design matrix.
lasso.coef              Measure an impact of the covariates on the
                        response using Lasso This function evaluates
                        the Lasso coefficients regressing 'y' onto the
                        design matrix 'x' over subsamples in
                        'subsamples'.
mcplus.coef             Measure an impact of the covariates on the
                        response using MC+. This function evaluates the
                        MC+ coefficients regressing 'y' onto the design
                        matrix 'x' over subsamples in 'subsamples'.
pearson.cor             Measure an impact of the covariates on the
                        response using Pearson correlatio. This
                        function evaluates the Pearson correlation
                        coefficient between the response 'y' and each
                        column in the design matrix 'x' over subsamples
                        in 'subsamples'.
rankings                Evaluate rankings
rbvs                    Ranking-Based Variable Selection
rbvs-package            Ranking-Based Variable Selection
s.est.quotient          Estimate the size of the top-ranked set
standardise             Standardise data
subsample               Generates subsamples.
top.ranked.sets         Find k-top-ranked sets
