External Control Borrowing for Rare Disease Trials R package rdborrow version 0.0.4.1 Implements causal inference methods for incorporating external control data into randomized controlled trials (RCTs) with longitudinal outcomes. Provides an analysis module supporting weighting-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW), difference-in-differences (DID), and synthetic control approaches for borrowing external control information, as well as a simulation module for generating trial and external control data, evaluating estimator performance via Monte Carlo studies, and conducting power analyses for sample size determination. Methods are based on Zhou et al. (2024) <doi:10.1093/jrsssa/qnae075> and Zhou et al. (2024) <doi:10.1080/10543406.2024.2330209>. Software Depends: R (>= 4.1.0) Imports: checkmate, futile.logger, mvtnorm, boot, Matrix, CVXR, copula, progress, stats, utils, methods Suggests: covr, ECOSolveR, knitr, pkgdown, rmarkdown, lintr, spelling, styler, testthat (>= 3.0.0) Matt Secrest <secrmatt@gmail.com> Comprehensive R Archive Network (CRAN) Lei Shi [aut], Matt Secrest [cre, aut] (ORCID: <https://orcid.org/0000-0002-0939-4902>), Herbert Pang [aut], Chen Chen [aut], Jiawen Zhu [aut], Genentech, Inc. [cph] Apache License (>= 2) 2026-09-24 application/tgz https://CRAN.R-project.org/package=rdborrow doi:10.32614/CRAN.package.rdborrow en-US