The package implements the conditionally optimal matching algorithm, which can be used to generate matched samples in designs with multiple treatment groups.
Currently, the algorithm can be applied to datasets with 3 to 6 groups and generates matched samples with one subject per group. The package provides functions to generate the matched sample and to evaluate the balance in key covariates.
The function implementing the matching algorithm is
polymatch. The algorithm is iterative and needs a matched
sample with one subject per group as starting point. This matched sample
can be automatically generated by polymatch or can be
provided by the user. The algorithm iteratively explores possible
reductions in the total distance of the matched sample.
Balance in key covariates can be evaluated with the function
balance. Given a matched sample and a set of covariates of
interest, the function computes the standardized differences and the
ratio of the variances for each pair of treatment groups in the study
design. For 3, 4, 5 and 6 groups, there are 3, 6, 10 and 15 pairs of
groups and the balance is evaluated before and after matching. The
result of balance can be graphically represented with
plotBalance.
You can install the package with the function
install_github of the package devtools.
library(devtools)
install_github("gnattino/polymatching")