data_precheck

Data precheck: Handle missing data(mean imputation) and remove redundant
columns; it also adds an NA column for indicating whether it's missing

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

data_precheck function

<dl><dt>X</dt>
<dd>a dataframe that the user initially inputs for matching - dataframe
with covariates</dd></dl>

Arguments

Data precheck: Handle missing data(mean imputation) and remove redundant
columns; it also adds an NA column for indicating whether it's missing — data_precheck

<dl>

<dt>X</dt>
<dd>a dataframe that the user initially inputs for matching - dataframe
with covariates</dd>

</dl>

data_precheck: Data precheck: Handle missing data(mean imputation) and remove redundant columns; it also adds an NA column for indicating whether it's missing

Description

Usage

Value

Arguments