It is often advantageous to test a hypothesis more than once in the context of propensity score analysis (Rosenbaum, 2012) <doi:10.1093/biomet/ass032>. 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.
|Depends:||ggplot2, graphics, PSAgraphics, R (≥ 3.0)|
|Imports:||ggthemes, Matching, MatchIt, modeltools, parallel, party, psych, reshape2, rpart, stats, TriMatch, utils|
|Author:||Jason Bryer [aut, cre]|
|Maintainer:||Jason Bryer <jason at bryer.org>|
|License:||GPL-2 | GPL-3 [expanded from: GPL]|
|CRAN checks:||PSAboot results|
Impact of Data Order for Propensity Score Matching
Bootstrapping for Propensity Score Analysiss
|Windows binaries:||r-devel: PSAboot_1.3.6.zip, r-release: PSAboot_1.3.6.zip, r-oldrel: PSAboot_1.3.6.zip|
|macOS binaries:||r-release (arm64): PSAboot_1.3.6.tgz, r-oldrel (arm64): PSAboot_1.3.6.tgz, r-release (x86_64): PSAboot_1.3.6.tgz, r-oldrel (x86_64): PSAboot_1.3.6.tgz|
|Old sources:||PSAboot archive|
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