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>.
| Version: |
1.0.1 |
| 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) |
| Published: |
2026-03-17 |
| DOI: |
10.32614/CRAN.package.TransHDM (may not be active yet) |
| Author: |
Huer Gao [aut, cre, cph],
Lulu Pan [aut, cph],
Yongfu Yu [ctb, cph],
Guoyou Qin [ctb, cph] |
| Maintainer: |
Huer Gao <gaohuer at proton.me> |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/Gaohuer/TransHDM |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
TransHDM citation info |
| CRAN checks: |
TransHDM results |
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