wzjwangzhijing.r-universe.dev

[zjwangatsu] transGFM 1.0.2

wangzhijing@sjtu.edu.cn (Zhijing Wang) — Thu, 08 Jan 2026 04:52:07 GMT

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.

[zjwang1013] sparseGFM 0.1.0

wangzhijing@sjtu.edu.cn (Zhijing Wang) — Mon, 15 Sep 2025 01:56:10 GMT

Implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) , Fan and Li (2001) , and Zhang (2010) .

[wzjwangzhijing] pECV 1.0.1

wangzhijing@sjtu.edu.cn (Zhijing Wang) — Thu, 28 Aug 2025 08:50:07 GMT

Implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models.