TransHDM
High-Dimensional Mediation Analysis via Transfer Learning
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
rolling linux/noble R-4.5 | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
rolling source/ R- | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
latest linux/jammy R-4.5 | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
latest linux/noble R-4.5 | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
latest source/ R- | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
2026-04-26 source/ R- | TransHDM_1.0.1.tar.gz |
511.0 KiB |
1.0.1 |
2026-04-23 source/ R- | TransHDM_1.0.1.tar.gz |
511.0 KiB |