  We consider the problem of constructing matched groups such that the
resulting groups are statistically similar with respect to their average values
for multiple covariates. This group-matching problem arises in many cases,
including quasi-experimental and observational studies in which subjects or
items are sampled from pre-existing groups, scenarios in which traditional
pair-matching approaches may be inappropriate. We consider the case in which
one is provided with an existing sample and iteratively eliminates samples so
that the groups "match" according to arbitrary statistically-defined criteria.
This problem is NP-hard. However, using artificial and real-world data sets, we
show that heuristics implemented by the ldamatch package produce high-quality
matches.
