PS-Integrated Methods for Incorporating Real-World Evidence in Clinical Studies R package psrwe version 3.2-1 High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. For example, propensity score (PS) methodology can be applied to select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of baseline covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, statistical methods such as the power prior approach or composite likelihood approach can be applied in each stratum to draw inference for the parameters of interest. This package provides functions that implement the PS-integrated real-world evidence analysis methods such as Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>, Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>. Software Depends: methods, R (>= 4.0), rstan (>= 2.26.0), Rcpp (>= 1.0.5) Imports: parallel (>= 3.2), cowplot (>= 1.0.0), dplyr (>= 0.8.5), ggplot2 (>= 3.3.2), randomForest (>= 4.6-14), survival, rstantools (>= 2.1.1) LinkingTo: BH (>= 1.72.0-3), rstan (>= 2.26.0), Rcpp (>= 1.0.5), RcppEigen (>= 0.3.3.7.0), StanHeaders (>= 2.26.0), RcppParallel (>= 5.0.2) Suggests: knitr, rmarkdown Wei-Chen Chen <wccsnow@gmail.com> Comprehensive R Archive Network (CRAN) Chenguang Wang [aut], Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R), Wei-Chen Chen [aut, cre] GPL (>= 3) 2026-02-18 application/tgz https://CRAN.R-project.org/package=psrwe doi:10.32614/CRAN.package.psrwe