transGFM: Transfer Learning for Generalized Factor Models

Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.

Version:	1.0.2
Depends:	R (≥ 3.5.0)
Imports:	stats
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-01-08
DOI:	10.32614/CRAN.package.transGFM
Author:	Zhijing Wang [aut, cre], Peirong Xu [aut], Hongyu Zhao [aut], Tao Wang [aut]
Maintainer:	Zhijing Wang <wangzhijing at sjtu.edu.cn>
BugReports:	https://github.com/zjwangATsu/transGFM/issues
License:	GPL-3
URL:	https://github.com/zjwangATsu/transGFM
NeedsCompilation:	no
CRAN checks:	transGFM results

Documentation:

Reference manual:

transGFM.html , transGFM.pdf

Downloads:

Package source:	transGFM_1.0.2.tar.gz
Windows binaries:	r-devel: transGFM_1.0.1.zip, r-release: transGFM_1.0.1.zip, r-oldrel: transGFM_1.0.1.zip
macOS binaries:	r-release (arm64): transGFM_1.0.2.tgz, r-oldrel (arm64): transGFM_1.0.2.tgz, r-release (x86_64): transGFM_1.0.2.tgz, r-oldrel (x86_64): transGFM_1.0.2.tgz
Old sources:	transGFM archive

Linking:

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