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transGFM (version 1.0.2)

Transfer Learning for Generalized Factor Models

Description

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.

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install.packages('transGFM')

Monthly Downloads

210

Version

1.0.2

License

GPL-3

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Maintainer

Zhijing Wang

Last Published

January 8th, 2026

Functions in transGFM (1.0.2)

transGFM_multi

Multiple source transfer learning for generalized factor models
source_potential

Identify potential sources based on rank comparison using IC criterion
transGFM

Single source transfer learning for generalized factor models
source_detection

Detect positive and negative transfer sources using ratio method
identify

Identify factor decomposition via SVD
ic_criterion

Information criterion (IC1/IC2) for selecting number of factors
relative_error

Calculate relative error between estimated and true matrices