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designmatch (version 0.1.1)

Construction of Optimally Matched Samples for Randomized Experiments and Observational Studies that are Balanced by Design

Description

Includes functions for the construction of matched samples that are balanced 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.

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Install

install.packages('designmatch')

Monthly Downloads

908

Version

0.1.1

License

GPL-2 | GPL-3

Maintainer

Jose Zubizarreta

Last Published

February 15th, 2016

Functions in designmatch (0.1.1)

designmatch-package

Optimal Matched Design of Randomized Experiments and Observational Studies
lalonde

Lalonde data set
finetab

Tabulate the marginal distribution of a nominal covariate after matching
absstddif

Absolute standardized differences in means.
distmat

Build a rank-based Mahalanobis distance matrix
bmatch

Optimal bipartite matching in observational studies
loveplot

Love plot for assessing covariate balance
nmatch

Optimal nonbipartite matching in randomized experiments and observational studies
meantab

Tabulate means of covariates after matching
pairsplot

Pairs plot for visualizing matched pairs
ecdfplot

Empirical cumulative distribution function plot for assessing covariate balance
germancities

Data from German cities before and after the Second World War