This method creates an object of type binary_experimental_design and will find pairs. You can then
use the function
resultsBinaryMatchSearch to create randomized allocation vectors. For one column
in X, we just sort to find the pairs trivially.
The design matrix with $n$ rows (one for each subject) and $p$ columns (one for each measurement on the subject). This is the design matrix you wish to search for a more optimal design.
The function that computes the distance matrix between every two observations in
An object of type
binary_experimental_design which can be further operated upon.
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