build.dist.struct_user

An internal helper function that generates the data abstraction for the edge
weights of the main network structure using the distance matrix passed by the
user.

Matching algorithm based on network-flow structure.
Users are able to modify the emphasis on three different
optimization goals: two different distance measures and
the number of treated units left unmatched. The method is proposed by Pimentel
and Kelz (2019) <doi:10.1080/01621459.2020.1720693>.
The 'rrelaxiv' package, which provides an alternative solver for
the underlying network flow problems, carries an
academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.

Shichao Han

MultiObjMatch

Multi-Objective Matching Algorithm

Samuel D. Pimentel

build.dist.struct_user function

<dl><dt>z</dt>
<dd>a vector indicating whether each unit is in treatment or control
group</dd>
<dt>distMat</dt>
<dd>a matrix of pair-wise distance</dd>
<dt>verbose</dt>
<dd>a boolean value whether to print(cat) debug information.
Default: FALSE</dd></dl>

Arguments

An internal helper function that generates the data abstraction for the edge
weights of the main network structure using the distance matrix passed by the
user. — build.dist.struct_user

<dl>

<dt>z</dt>
<dd>a vector indicating whether each unit is in treatment or control
group</dd>


<dt>distMat</dt>
<dd>a matrix of pair-wise distance</dd>


<dt>verbose</dt>
<dd>a boolean value whether to print(cat) debug information.
Default: FALSE</dd>

</dl>

An internal helper function that generates the data abstraction for the edge
weights of the main network structure using the distance matrix passed by the
user.

build.dist.struct_user: An internal helper function that generates the data abstraction for the edge weights of the main network structure using the distance matrix passed by the user.

Description

Usage

Value

Arguments