glmtrans
Transfer Learning under Regularized Generalized Linear Models
We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1.0 |
rolling source/ R- | glmtrans_2.1.0.tar.gz |
324.9 KiB |
2.1.0 |
rolling linux/jammy R-4.5 | glmtrans_2.1.0.tar.gz |
395.1 KiB |
2.1.0 |
rolling linux/noble R-4.5 | glmtrans_2.1.0.tar.gz |
395.0 KiB |
2.1.0 |
latest source/ R- | glmtrans_2.1.0.tar.gz |
324.9 KiB |
2.1.0 |
latest linux/jammy R-4.5 | glmtrans_2.1.0.tar.gz |
395.1 KiB |
2.1.0 |
latest linux/noble R-4.5 | glmtrans_2.1.0.tar.gz |
395.0 KiB |
2.1.0 |
2026-04-23 source/ R- | glmtrans_2.1.0.tar.gz |
324.9 KiB |
2.1.0 |
2026-04-09 windows/windows R-4.5 | glmtrans_2.1.0.zip |
397.4 KiB |
2.1.0 |
2025-04-20 source/ R- | glmtrans_2.1.0.tar.gz |
324.9 KiB |