It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012). The functions in this package facilitate bootstrapping for propensity score analysis. By default, bootstrapping using two classification tree methods (using rpart and ctree functions), two matching methods (using Matching and MatchIt packages), and stratification with logistic regression are used. Framework is provided for users to implement additional methods. Visualizations are emphasized for diagnosing balance; exploring the correlation relationships between bootstrap samples and methods; and to summarize results.
|Author||Jason Bryer <[email protected]>|
|Maintainer||Jason Bryer <[email protected]>|
|Package repository||View on GitHub|
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