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 models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is available. We also provides visualization for the transferable source detection results. A relevant paper by Ye Tian and Yang Feng (2021) will be available soon on arXiv.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | glmnet, ggplot2, foreach, doParallel, caret, assertthat, formatR, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2021-04-28 |
| 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 |
| Reference manual: | glmtrans.pdf |
| Vignettes: |
glmtrans-demo |
| Package source: | glmtrans_1.0.0.tar.gz |
| Windows binaries: | r-devel: glmtrans_1.0.0.zip, r-release: glmtrans_1.0.0.zip, r-oldrel: glmtrans_1.0.0.zip |
| macOS binaries: | r-release (arm64): glmtrans_1.0.0.tgz, r-release (x86_64): glmtrans_1.0.0.tgz, r-oldrel: glmtrans_1.0.0.tgz |
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