glmtrans: Transfer Learning under Regularized Generalized Linear Models

We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".

Version:	2.1.0
Depends:	R (≥ 3.5.0)
Imports:	glmnet, ggplot2, foreach, doParallel, caret, assertthat, formatR, stats
Suggests:	knitr, rmarkdown
Published:	2025-03-01
DOI:	10.32614/CRAN.package.glmtrans
Author:	Ye Tian [aut, cre], Yang Feng [aut]
Maintainer:	Ye Tian <ye.t at columbia.edu>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	glmtrans results

Documentation:

Reference manual:	glmtrans.html , glmtrans.pdf
Vignettes:	glmtrans-demo (source, R code)

Downloads:

Package source:	glmtrans_2.1.0.tar.gz
Windows binaries:	r-devel: glmtrans_2.1.0.zip, r-release: glmtrans_2.1.0.zip, r-oldrel: glmtrans_2.1.0.zip
macOS binaries:	r-release (arm64): glmtrans_2.1.0.tgz, r-oldrel (arm64): glmtrans_2.1.0.tgz, r-release (x86_64): glmtrans_2.1.0.tgz, r-oldrel (x86_64): glmtrans_2.1.0.tgz
Old sources:	glmtrans archive

Reverse dependencies:

Reverse suggests:

sparselink, transreg

Linking:

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