Package: glmtrans
Type: Package
Title: Transfer Learning under Regularized Generalized Linear Models
Version: 2.1.0
Authors@R: c(person("Ye", "Tian", role = c("aut", "cre"), email = "ye.t@columbia.edu"), person("Yang", "Feng", role = "aut", email = "yang.feng@nyu.edu"))
Description: 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.".
Imports: glmnet, ggplot2, foreach, doParallel, caret, assertthat,
        formatR, stats
License: GPL-2
Depends: R (>= 3.5.0)
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-02-28 17:52:40 UTC; yetian
Author: Ye Tian [aut, cre],
  Yang Feng [aut]
Maintainer: Ye Tian <ye.t@columbia.edu>
Repository: CRAN
Date/Publication: 2025-03-01 01:40:08 UTC
Built: R 4.6.0; ; 2026-04-25 13:42:56 UTC; unix
