The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <doi:10.48550/arXiv.1610.00667>.
| Version: | 0.2.1 |
| Depends: | R (≥ 3.5.0) |
| Suggests: | knitr, rmarkdown, MASS, ggplot2, mvtnorm |
| Published: | 2022-04-24 |
| DOI: | 10.32614/CRAN.package.FusionLearn |
| Author: | Xin Gao, Yuan Zhong, and Raymond J. Carroll |
| Maintainer: | Yuan Zhong <aqua.zhong at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | FusionLearn results |
| Reference manual: | FusionLearn.pdf |
| Vignettes: |
Fusion Learning Vignette |
| Package source: | FusionLearn_0.2.1.tar.gz |
| Windows binaries: | r-devel: FusionLearn_0.2.1.zip, r-release: FusionLearn_0.2.1.zip, r-oldrel: FusionLearn_0.2.1.zip |
| macOS binaries: | r-release (arm64): FusionLearn_0.2.1.tgz, r-oldrel (arm64): FusionLearn_0.2.1.tgz, r-release (x86_64): FusionLearn_0.2.1.tgz, r-oldrel (x86_64): FusionLearn_0.2.1.tgz |
| Old sources: | FusionLearn archive |
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