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sparseSEM

4.1

Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework

0packages depend
6.5Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Anhui HuangFirst published 2014-08-267 releasesCRAN page ↗

Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM.

Install

Health

CRAN checks
13OK
Slowest check: 4.4 min · r-release-macos-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
1
Dependencies · direct
Check history
  • OK2026-08-04
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 315 wordsVignettesYes · dynamicpkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
not tracked
Return-value docs
not tracked
References docs
92%

Downloads

6.5K
CRAN downloads in the past year
Rank #6,759 · ~18/day · ~540/mo
Daily download trend is not available in this view yet.
26230 days
1.2K90 days
6.5K1 year
Compare downloads with other packages →
Also on387 r2u37 autocran89 c2d4u

Dependencies

Declared dependencies
2 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.5.0
Imports (1)
parallel
LinkingTo (0)
none
Suggests (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (1)
Maintainer (1)
Author, Contributor, Maintainer · added in 2.5
Authors (1)
Author, Contributor, Maintainer · added in 2.5
Contributors (1)
Author, Contributor, Maintainer · added in 2.5
Listed in earlier versions (1)
no longer listed · 2.3
Package Timeline

7 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • 4.1Latest
    2024-10-27 · current release · diff ↗
  • R
    R 4.4.0 released · 2024-04-24
  • 4.0
    2023-08-09 · diff ↗
  • 3.8-2
    2023-06-11 · diff ↗
  • 3.8-1
    2023-05-04 · diff ↗
  • 3.8
    2023-04-21 · diff ↗
  • unarchivedReturned to CRAN
    2023-04-21
  • R
    R 4.3.0 released · 2023-04-21
  • archivedRemoved from CRAN
    2022-05-07
    installation issues were not corrected in time
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
Show 6 earlier events
  • R
    R 3.4.0 released · 2017-04-21
  • R
    R 3.3.0 released · 2016-05-03
  • R
    R 3.2.0 released · 2015-04-16
  • 2.5
    2014-09-04 · diff ↗
  • 2.3
    2014-08-26
  • R
    R 3.1.0 released · 2014-04-10

Package metadata

First published
2014-08-26
Total releases
7 / 12 yrs
License
GPL
Minimum R
≥ 3.5.0
Bundled data
860 KB / 5 files
Download size
4.5 MB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("sparseSEM")
Huang, A. (2024). sparseSEM: Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework (Version 4.1) [Computer software]. https://doi.org/10.32614/CRAN.package.sparseSEM

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

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 sparseSEM version 4.1 [Data set]. HJJB, LLC. Data release v2026-08-26. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-26, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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