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Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats |
| Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: | 2025-11-13 |
| DOI: | 10.32614/CRAN.package.transGFM |
| Author: | Zhijing Wang [aut, cre] |
| Maintainer: | Zhijing Wang <wangzhijing at sjtu.edu.cn> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | transGFM results |
| Reference manual: | transGFM.html , transGFM.pdf |
| Package source: | transGFM_1.0.1.tar.gz |
| Windows binaries: | r-devel: transGFM_1.0.1.zip, r-release: transGFM_1.0.1.zip, r-oldrel: transGFM_1.0.1.zip |
| macOS binaries: | r-release (arm64): transGFM_1.0.1.tgz, r-oldrel (arm64): transGFM_1.0.1.tgz, r-release (x86_64): transGFM_1.0.1.tgz, r-oldrel (x86_64): transGFM_1.0.1.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.