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 'GLPK' 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 http://user.gurobi.com/download/gurobi-optimizer and http://www.gurobi.com/documentation/6.5/refman/r_api_overview.html. We have also included directions in the gurobi_installation file in the inst folder.
|Author||Jose R. Zubizarreta <firstname.lastname@example.org>, Cinar Kilcioglu <email@example.com>|
|Date of publication||2016-08-11 13:01:07|
|Maintainer||Jose R. Zubizarreta <firstname.lastname@example.org>|
|License||GPL-2 | GPL-3|
absstddif: Absolute standardized differences in means.
bmatch: Optimal bipartite matching in observational studies
designmatch-package: Optimal Matched Design of Randomized Experiments and...
distmat: Build a rank-based Mahalanobis distance matrix
ecdfplot: Empirical cumulative distribution function plot for assessing...
finetab: Tabulate the marginal distribution of a nominal covariate...
germancities: Data from German cities before and after the Second World War
lalonde: Lalonde data set
loveplot: Love plot for assessing covariate balance
meantab: Tabulate means of covariates after matching
nmatch: Optimal nonbipartite matching in randomized experiments and...
pairsplot: Pairs plot for visualizing matched pairs