that predictor with the response.

Then, computing the change in redundancy involves finding the mutual information between the given predictor and the predictors already selected. The predictor with largest differential $\Phi$ is then added to the model. The complexity of this redundancy computation is likely $O(N^2 \cdot k)$ where $k$ is the current size of the model. This method produces an ordered list of features which can be used to build a model of any size up to the user-chosen stopping size.

Although the asymptotic complexity is $O(N^2 \cdot p)$ for sample size $N$ and total number of features $p$, as the size of the current model increases, it becomes more expensive to compute each iteration causing the selection of 50 features in a Leave-One-Subject-Out setting to require 28 hours on 2.2 GHz Intel Core i7 processor using the `pymrmr` package.

Distance Correlation
--------------------

In Székely et al. [@dcorr], the authors present a measurement of dependence analogous to the Pearson correlation coefficient but instead of mere lack of correlation at a value of zero, it ensures independence instead. By measuring the distances (typically Euclidean) between data points and finding the correlation of these distances between two variates, distance correlation is 