initBinaryMatchExperimentalDesignSearch

This method creates an object of type pairwise_matching_experimental_design_search and will immediately initiate
a search through $1_T$ space for pairwise match designs based on the structure computed in the function <code>computeBinaryMatchStructure</code>. 
For debugging, you can use set the <code>seed</code> parameter and <code>num_cores = 1</code> to be assured of deterministic output.

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

initBinaryMatchExperimentalDesignSearch function

<dl><dt>binary_match_structure</dt>
<dd>The <code>binary_experimental_design</code> object where the pairs are computed.</dd>
<dt>max_designs</dt>
<dd>How many random allocation vectors you wish to return. The default is 1000.</dd>
<dt>wait</dt>
<dd>Should the <code>R</code> terminal hang until all <code>max_designs</code> vectors are found? The 
default is <code>FALSE</code>.</dd>
<dt>start</dt>
<dd>Should we start searching immediately (default is <code>TRUE</code>).</dd>
<dt>num_cores</dt>
<dd>The number of CPU cores you wish to use during the search. The default is <code>1</code>.</dd>
<dt>seed</dt>
<dd>The set to set for deterministic output. This should only be set if <code>num_cores = 1</code> otherwise
the output will not be deterministic. Default is <code>NULL</code> for no seed set.</dd>
<dt>prop_flips</dt>
<dd>Proportion of flips. Default is all. Lower for more correlated assignments (useful for research only).</dd></dl>

Arguments

Author

Begin a Binary Match Search — initBinaryMatchExperimentalDesignSearch

<dl>

<dt>binary_match_structure</dt>
<dd>The <code>binary_experimental_design</code> object where the pairs are computed.</dd>


<dt>max_designs</dt>
<dd>How many random allocation vectors you wish to return. The default is 1000.</dd>


<dt>wait</dt>
<dd>Should the <code>R</code> terminal hang until all <code>max_designs</code> vectors are found? The 
default is <code>FALSE</code>.</dd>


<dt>start</dt>
<dd>Should we start searching immediately (default is <code>TRUE</code>).</dd>


<dt>num_cores</dt>
<dd>The number of CPU cores you wish to use during the search. The default is <code>1</code>.</dd>


<dt>seed</dt>
<dd>The set to set for deterministic output. This should only be set if <code>num_cores = 1</code> otherwise
the output will not be deterministic. Default is <code>NULL</code> for no seed set.</dd>


<dt>prop_flips</dt>
<dd>Proportion of flips. Default is all. Lower for more correlated assignments (useful for research only).</dd>

</dl>

initBinaryMatchExperimentalDesignSearch: Begin a Binary Match Search

Description

Usage

Arguments

Author