initBinaryMatchFollowedByRerandomizationDesignSearch

This method creates an object of type binary_then_rerandomization_experimental_design and will find optimal matched pairs which
are then rerandomized in order to further minimize a balance metric. You can then
use the function <code>resultsBinaryMatchThenRerandomizationSearch</code> to obtain the randomized allocation vectors. For one column
in X, the matching just sorts the values 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

initBinaryMatchFollowedByRerandomizationDesignSearch 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>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>...</dt>
<dd>Arguments passed to <code>initGreedyExperimentalDesignObject</code>. It is recommended to set
<code>max_designs</code> otherwise it will default to 10,000.</dd></dl>

Arguments

Author

Begin a Search for Binary Matching Followed by Rerandomization — initBinaryMatchFollowedByRerandomizationDesignSearch

<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>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>...</dt>
<dd>Arguments passed to <code>initGreedyExperimentalDesignObject</code>. It is recommended to set
<code>max_designs</code> otherwise it will default to 10,000.</dd>

</dl>

initBinaryMatchFollowedByRerandomizationDesignSearch: Begin a Search for Binary Matching Followed by Rerandomization

Description

Usage

Value

Arguments

Author