Frequentist Dynamic Borrowing for Hybrid-Control Survival Trials R package fdb version 0.2.0 Implements a class of likelihood-informed frequentist dynamic borrowing methods for hybrid-control survival trials based on penalized Cox partial likelihood estimation. Implements four likelihood-informed penalty structures (precision-weighted L1, smoothed integrated-gate, information-adaptive minimax concave penalty (MCP), and likelihood-ratio-weighted L1), together with the adaptive lasso borrowing approach of Li et al. (2023, <doi:10.1002/bimj.202100406>). Provides conditional model-based standard errors and local plug-in sandwich variance approximations, with smoothed penalties. Tools for design-stage lambda calibration via simulation, including a two-stage coarse-fine grid search, drift-level early stopping, and per-method tuning under both inference types, are also provided. A simulation harness for evaluating type I error and statistical power across population drift scenarios is included. Software Depends: R (>= 3.6.0) Imports: survival, stats, parallel, utils Suggests: testthat (>= 3.0.0), knitr, rmarkdown Yusuke Yamaguchi <yamagubed@gmail.com> Comprehensive R Archive Network (CRAN) Yusuke Yamaguchi [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=fdb/LICENSE) 2026-10-04 application/tgz https://CRAN.R-project.org/package=fdb doi:10.32614/CRAN.package.fdb