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: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=glmtrans to link to this page.
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