Package: sparseGFM
Type: Package
Title: Sparse Generalized Factor Models with Multiple Penalty Functions
Version: 0.1.0
Authors@R: person("Zhijing", "Wang", email = "wangzhijing@sjtu.edu.cn", role = c("aut", "cre"))
Description: 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) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.
License: GPL (>= 3)
Encoding: UTF-8
Imports: stats, GFM, MASS, irlba
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
RoxygenNote: 7.3.2
URL: https://github.com/zjwang1013/sparseGFM
BugReports: https://github.com/zjwang1013/sparseGFM/issues
NeedsCompilation: no
Packaged: 2025-09-05 16:12:02 UTC; clswt-wangzhijing
Author: Zhijing Wang [aut, cre]
Maintainer: Zhijing Wang <wangzhijing@sjtu.edu.cn>
Repository: CRAN
Date/Publication: 2025-09-10 08:30:02 UTC
Built: R 4.6.0; ; 2026-04-27 20:23:08 UTC; unix
