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Generalized Factor Model

4packages depend
3.4Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables. Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model, respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. This model can be employed in social and behavioral sciences, economy and finance, and genomics, to extract interpretable nonlinear factors. More details can be referred to Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2023) doi:10.1080/01621459.2021.1999818.

Install

Health

CRAN checks
13OK
Slowest check: 3.7 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-04-22
  • ERROR13 OK, 1 ERROR2026-04-18
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 1.2.2
READMEYes · 436 wordsVignettesYes · 4 · dynamicNEWSNoCitation fileNo
Help pages
7
Examples that run
100%7 run in a plain check
Exported functions with examples
100%
Documented parameters
98%
Return-value docs
100%
Help pages citing references
43%

Downloads

3.4K
CRAN downloads in the past year
Rank #10,372 · ~9/day · ~283/mo
Daily download trend is not available in this view yet.
44830 days
97790 days
3.4K1 year
Compare downloads with other packages →
Also on395 r2u7 autocran

Repository

2Stars
3Forks
0Open issues
0Open PRs
0Releases
30Commits
1Commit identities
nonlinear-dimension-reductionapproximate-factor-modelnumber-of-factorsfeature-extraction
30 commits · Last activity 2026-01-17
Responsiveness
not measured

Stars over time

2022-06-14 · 22026-07-07 · 2
PracticesBeta

3 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/feiyoung/gfm on 2026-10-11.

Docs source (2)
pkgdown

GitHub Pages site last deployed 2026-01-17

Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
7 external dependencies (excludes base and recommended)

Installing GFM adds 7 packages (3 with compiled code, 3.6 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, irlba 375 KB.

Depends (3)
R >= 3.5.0doSNOWparallel
Imports (5)
MASSstatsirlbaRcppmethods
LinkingTo (2)
Suggests (2)
Enhances (0)
none
Reverse dependencies
4direct
0indirect
Imports 4
Depends, Imports or LinkingTo, most downloaded first

People & History

People (5)
Author, Maintainer since 1.1.0 (2022)
Author
Author · added in 1.2.1
Releases (4)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.2.2Latest
    record
    2026-01-18 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • 1.2.1
    record
    2023-08-11 · diff ↗ ·
    added 2 exports
    overdispersedGFM, OverGFMchooseFacNumber
  • R
    R 4.3.0 released · 2023-04-21
  • 1.2.0
    record
    2023-02-13 · diff ↗ ·
    removed 1 export
    singleIC
    added 1 export
    chooseFacNumber
  • R
    R 4.2.0 released · 2022-04-22
  • 1.1.0
    record
    2022-01-05
  • R
    R 4.1.0 released · 2021-05-18

Package metadata

First published
2022-01-05
Total releases
4 / 4 yrs
License
GPL-3 OSI
Minimum R
≥ 3.5.0
Download size
351 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("GFM")
Liu, W., Lin, H., Liu, J., Nie, J., & Zheng, S. (2026). GFM: Generalized Factor Model (Version 1.2.2) [Computer software]. https://doi.org/10.32614/CRAN.package.GFM

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for GFM version 1.2.2 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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