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 relevant paper
is available on arXiv: <arXiv:2105.14328>.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
glmnet, ggplot2, foreach, doParallel, caret, assertthat, formatR, stats |
| Suggests: |
knitr, rmarkdown |
| Published: |
2022-02-08 |
| 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 |
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