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Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) <https://jmlr.org/papers/v24/23-0030.html>.
Version: | 0.1.0 |
Depends: | R (≥ 2.10) |
Imports: | caret, formatR, MASS, quadprog, splines, stats |
Suggests: | knitr, rmarkdown |
Published: | 2024-01-12 |
DOI: | 10.32614/CRAN.package.matrans |
Author: | Xiaonan Hu |
Maintainer: | Xiaonan Hu <xiaonanhu at cnu.edu.cn> |
License: | GPL (≥ 3) |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | matrans results |
Reference manual: | matrans.pdf |
Vignettes: |
matrans-vignette |
Package source: | matrans_0.1.0.tar.gz |
Windows binaries: | r-devel: matrans_0.1.0.zip, r-release: matrans_0.1.0.zip, r-oldrel: matrans_0.1.0.zip |
macOS binaries: | r-release (arm64): matrans_0.1.0.tgz, r-oldrel (arm64): matrans_0.1.0.tgz, r-release (x86_64): matrans_0.1.0.tgz, r-oldrel (x86_64): matrans_0.1.0.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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