nbpMatching-package

nbpMatching

This package will take an input distance matrix and generate the set of
pairwise matches that minimizes the sum of distances between the pairs by
running nonbimatch.

package

cluster

array

Perform non-bipartite matching and matched randomization. A
"bipartite" matching utilizes two separate groups, e.g. smokers being
matched to nonsmokers or cases being matched to controls. A "non-bipartite"
matching creates mates from one big group, e.g. 100 hospitals being
randomized for a two-arm cluster randomized trial or 5000 children who
have been exposed to various levels of secondhand smoke and are being
paired to form a greater exposure vs. lesser exposure comparison. At the
core of a non-bipartite matching is a N x N distance matrix for N potential
mates. The distance between two units expresses a measure of similarity or
quality as mates (the lower the better). The 'gendistance()' and
'distancematrix()' functions assist in creating this. The 'nonbimatch()'
function creates the matching that minimizes the total sum of distances
between mates; hence, it is referred to as an "optimal" matching. The
'assign.grp()' function aids in performing a matched randomization. Note
bipartite matching can be performed using the prevent option in
'gendistance()'.

Cole Beck

Functions for Optimal Non-Bipartite Matching

Bo Lu

Robert Greevy

nbpMatching-package function

Bo Lu, Robert Greevy, Cole Beck
Maintainer: Cole Beck <a href="/link/cole.beck%40vumc.org?package=nbpMatching&version=1.5.6" data-mini-rdoc="nbpMatching::cole.beck@vumc.org">cole.beck@vumc.org</a>

Author

Nonbipartite Matching — nbpMatching-package

Bo Lu, Robert Greevy, Cole Beck
Maintainer: Cole Beck <a href='mailto:cole.beck@vumc.org'>cole.beck@vumc.org</a>

nbpMatching-package: Nonbipartite Matching

Description

Arguments

Author

Details

References

See Also