FusionLearn: Fusion Learning

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

Documentation:

Reference manual:	FusionLearn.pdf
Vignettes:	Fusion Learning Vignette

Downloads:

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

Linking:

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