Transfer Learning for Generalized Factor Models
Information criterion (IC1/IC2) for selecting number of factors
Identify factor decomposition via SVD
Calculate relative error between estimated and true matrices
Detect positive and negative transfer sources using ratio method
Identify potential sources based on rank comparison using IC criterion
Multiple source transfer learning for generalized factor models
Single source 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.