Given a sample \(X_1, X_2,\dots, X_n\) from a continuous density function \(f(x)\) and distribution function \(F(x)\). \(\breve{\eta}\) is defined by $$\breve{\eta}=-\frac{\sum_{i=1}^n {U_iW_i}-n\bar{U}\bar{W}}{\sqrt{(\sum_{i=1}^n {U_i^2-n\bar{U^2}})(\sum_{i=1}^n {W_i^2-n\bar{W^2}})}}$$
where
$$U_i = \hat{f}(X_i), \; W_i =F_n(X_i), \; \bar{U}=n^{-1}\sum_{i=1}^n U_i, \; \bar{W}=n^{-1}\sum_{i=1}^n W_i.$$
eta.w.breve.bc
uses reflection to correct the boundary bias of the kernel density estimate kde