Description: Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies (Hansen and Klopfer 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.
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The optmatch package implements the optimal full matching algorithm for bipartite matching problems. Given a matrix describing the distances between two groups (where one group is represented by row entries, and the other by column entries), the algorithm finds a matching between units that minimizes the average within grouped distances. This algorithm is a popular choice for covariate balancing applications (e.g. propensity score matching), but it also can be useful for design stage applications such as bl
Hansen, B.B. and Klopfer, S.O. ( 2006 ) Optimal full matching and related designs via network flows, JCGS 15 609 -627. optmatch is available on CRAN :