High-Dimensional Mediation Analysis via Transfer Learning R package TransHDM version 1.0.1 Provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>. Software Depends: R (>= 4.0.0) Imports: glmnet (>= 4.1-10), caret (>= 7.0-1), MASS (>= 7.3-61), doParallel (>= 1.0.17), foreach (>= 1.5.2), HDMT (>= 1.0.5) Suggests: knitr (>= 1.50), rmarkdown (>= 2.30), spelling (>= 2.3.2) Huer Gao <gaohuer@proton.me> Comprehensive R Archive Network (CRAN) Huer Gao [aut, cre, cph], Lulu Pan [aut, cph], Yongfu Yu [ctb, cph], Guoyou Qin [ctb, cph] GPL (>= 3) 2026-03-17 application/tgz https://CRAN.R-project.org/package=TransHDM doi:10.32614/CRAN.package.TransHDM en-US