Matched Samples that are Balanced and Representative by Design
Absolute standardized differences in means.
Optimal bipartite matching in observational studies
Optimal cardinality matching in observational studies
Optimal Matched Design of Randomized Experiments and Observational Stu...
Build a rank-based Mahalanobis distance matrix
Optimal distance matching in observational studies
Empirical cumulative distribution function plot for assessing covariat...
Tabulate the marginal distribution of a nominal covariate after matchi...
Love plot for assessing covariate balance
Tabulate means of covariates after matching
Optimal nonbipartite matching in randomized experiments and observatio...
Pairs plot for visualizing matched pairs
Optimal profile matching
Includes functions for the construction of matched samples that are balanced and representative by design. Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface. For their installation, please follow the instructions at <https://www.gurobi.com/documentation/quickstart.html> and <https://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>. We have also included directions in the gurobi_installation file in the inst folder.