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 (PSA). 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. A framework
is described for users to implement additional propensity score
methods. Visualizations are emphasized for diagnosing balance;
exploring the correlation relationships between bootstrap samples and
methods; and to summarize results.