R Observatory: Metrics for sparseGFM version 0.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

sparseGFM

0.1.0Package record

Sparse Generalized Factor Models with Multiple Penalty Functions

0packages depend

5.4Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Zhijing WangCRAN page ↗GitHub ↗Issues ↗

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.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("sparseGFM", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("zjwang1013/sparseGFM")

Development version from zjwang1013/sparseGFM. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-sparsegfm

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-sparseGFM

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-sparseGFM

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 1.7 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-04
NOTE12 OK, 1 NOTE2026-08-01
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.1.0

READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION

Help pages: 6
Examples that run: 20%1 run in a plain check · 4 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 83%
Documented parameters: 94%
Return-value docs: 100%
Help pages citing references: 0%

How these are measured

Downloads

5.4K

CRAN downloads in the past year

Rank #7,321 · ~15/day · ~453/mo

Releases R versions

Daily download trend is not available in this view yet.

35330 days

1.2K90 days

5.4K1 year

Compare downloads with other packages →

Also on53 r2u2 autocran

How downloads are counted

Repository

zjwang1013/sparsegfm ↗Account: zjwang1013

2Stars

1Forks

0Open issues

0Open PRs

1Releases

14Commits

1Commit identities

dimension-reductionfactor-modelspenalized-regressionvariable-selection

14 commits · Last activity 2025-09-15 · 0% stars, 30d

Responsiveness

not measured

Stars over time

2025-09-11 · 12026-07-07 · 2

How these are measured

PracticesBeta

1 development-tooling and community-health practice detected across 1 family in the upstream repository

Checks run against github.com/zjwang1013/sparsegfm on 2026-10-21.

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

5 external dependencies (excludes base and recommended)

Installing sparseGFM adds 8 packages (4 with compiled code, 3.9 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, irlba 375 KB.

Depends (0)

none

Imports (4)

statsGFMMASSirlba

LinkingTo (0)

none

Suggests (3)

knitr rmarkdown testthat

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (1)

Zhijing Wang

Author, Maintainer since 0.1.0 (2025)

Releases (1)

R
R 4.6.0 released · 2026-04-24
0.1.0Latest
record
2025-09-10 · current release
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2025-09-10
Total releases: 1 / 1 yrs
License: GPL (>= 3) OSI
Download size: 13 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("sparseGFM")

Format

Wang, Z. (2025). sparseGFM: Sparse Generalized Factor Models with Multiple Penalty Functions (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.sparseGFM

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.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for sparseGFM version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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