qualities and select the $k$ best using any standard sorting algorithm, which is $O(p \log p)$ for $p$ total predictors. Since the data is not modified in this process and the predictors are not compared against one another, the $p$ distance correlation computations can be computed in parallel. Since we must compute $p$ separate distance correlations, the computational complexity is $O(N^2 \cdot p)$, but sometimes can be saved by sharing the distance matrix of the response across the computations of each correlation. Similar savings could be exploited across folds in a jackknife setting, but this was not implemented.

The runtime advantage of DC-SIS may not be generalizable. However, One peculiar feature of this PD dataset is that it is only as long as it is wide. Consequently, methods that are bottle-necked by the number of features perform the same as those bottle-necked by the number of samples. Distance correlation has complexity $O(N^2)$, and is thus intended for small datasets where the asymptotic results provided by the central limit theorem do not apply. As the number of samples increases, this method quickly becomes untenable.

Additional Datasets
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To further demonstrate the computational properties of mRMR and DC-SIS, we perform a similar 