computeBinaryMatchStructure

This method creates an object of type binary_match_structure and will compute pairs. You can then
use the functions <code>initBinaryMatchExperimentalDesignSearch</code> and <code>resultsBinaryMatchSearch</code> 
to create randomized allocation vectors. For one column in X, we just sort to find the pairs trivially.

Computes experimental designs for a
two-arm experiment with covariates via a number of methods:
(0) complete randomization and randomization with forced-balance,
(1) Greedily optimizing a
balance objective function via pairwise switching. This optimization
provides lower variance for the treatment effect estimator (and higher
power) while preserving a design that is close to complete randomization.
We return all iterations of the designs for use in a permutation test,
(2) The second is via numerical optimization
(via 'gurobi' which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>)
a la Bertsimas and Kallus,
(3) rerandomization,
(4) Karp's method for one covariate,
(5) exhaustive enumeration to find the
optimal solution (only for small sample sizes),
(6) Binary pair matching using the 'nbpMatching' library,
(7) Binary pair matching plus design number (1) to further optimize balance,
(8) Binary pair matching plus design number (3) to further optimize balance,
(9) Hadamard designs,
(10) Simultaneous Multiple Kernels.
In (1-9) we allow for three objective functions:
Mahalanobis distance,
Sum of absolute differences standardized and
Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in
Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>,
Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance
Optimization" (2021) <arXiv:2012.03330>.

Adam Kapelner

GreedyExperimentalDesign

Greedy Experimental Design Construction

David Azriel

Abba Krieger

computeBinaryMatchStructure function

<dl><dt>X</dt>
<dd>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.</dd>
<dt>mahal_match</dt>
<dd>Match using Mahalanobis distance. Default is <code>FALSE</code>.</dd>
<dt>compute_dist_matrix</dt>
<dd>The function that computes the distance matrix between every two observations in <code>X</code>, 
its only argument. The default is <code>NULL</code> signifying euclidean squared distance optimized in C++.</dd>
<dt>D</dt>
<dd>A distance matrix precomputed. The default is <code>NULL</code> indicating the distance matrix should be computed.</dd></dl>

Arguments

Author

Compute Binary Matching Strcuture — computeBinaryMatchStructure

<dl>

<dt>X</dt>
<dd>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.</dd>


<dt>mahal_match</dt>
<dd>Match using Mahalanobis distance. Default is <code>FALSE</code>.</dd>


<dt>compute_dist_matrix</dt>
<dd>The function that computes the distance matrix between every two observations in <code>X</code>, 
its only argument. The default is <code>NULL</code> signifying euclidean squared distance optimized in C++.</dd>


<dt>D</dt>
<dd>A distance matrix precomputed. The default is <code>NULL</code> indicating the distance matrix should be computed.</dd>

</dl>

computeBinaryMatchStructure: Compute Binary Matching Strcuture

Description

Usage

Value

Arguments

Author