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Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.
| Version: | 1.0.0 |
| Imports: | gbm, glmnet, nnet, randomForest, stats |
| Suggests: | knitr, rmarkdown |
| Published: | 2024-10-24 |
| DOI: | 10.32614/CRAN.package.ARTtransfer |
| Author: | Boxiang Wang [aut, cre], Yunan Wu [aut], Chenglong Ye [aut] |
| Maintainer: | Boxiang Wang <boxiang-wang at uiowa.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | ARTtransfer results |
| Reference manual: | ARTtransfer.pdf |
| Vignettes: |
Introduction to ARTtransfer (source) |
| Package source: | ARTtransfer_1.0.0.tar.gz |
| Windows binaries: | r-devel: ARTtransfer_1.0.0.zip, r-release: ARTtransfer_1.0.0.zip, r-oldrel: ARTtransfer_1.0.0.zip |
| macOS binaries: | r-release (arm64): ARTtransfer_1.0.0.tgz, r-oldrel (arm64): ARTtransfer_1.0.0.tgz, r-release (x86_64): ARTtransfer_1.0.0.tgz, r-oldrel (x86_64): ARTtransfer_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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