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.

A collection of functions that primarily produce graphics to
    aid in a Propensity Score Analysis (PSA).  Functions include: cat.psa
    and box.psa to test balance within strata of categorical and
    quantitative covariates, circ.psa for a representation of the
    estimated effect size by stratum, loess.psa that provides a graphic
    and loess based effect size estimate, and various balance functions
    that provide measures of the balance achieved via a PSA in a
    categorical covariate.

Connects to the API of <https://pushshift.io/> to search for 'Reddit' comments and submissions.

Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. 'psBayesborrow' is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application.