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.".
| Version: | 2.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | glmnet,ggplot2,foreach,doParallel,caret,assertthat,formatR, stats |
| Suggests: | knitr,rmarkdown |
| Published: | 2025-03-01 |
| DOI: | 10.32614/CRAN.package.glmtrans |
| Author: | Ye Tian [aut, cre], Yang Feng [aut] |
| Maintainer: | Ye Tian <ye.t at columbia.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | glmtrans results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=glmtransto link to this page.