lslx

Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood

https://github.com/psyphh/lslx

Science Score: 33.0%

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  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
    Links to: springer.com
  • Committers with academic emails
    1 of 5 committers (20.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (8.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood

Basic Info
  • Host: GitHub
  • Owner: psyphh
  • License: gpl-3.0
  • Language: R
  • Default Branch: ver.0.6.11
  • Homepage:
  • Size: 5.66 MB
Statistics
  • Stars: 6
  • Watchers: 1
  • Forks: 4
  • Open Issues: 0
  • Releases: 0
Created almost 9 years ago · Last pushed about 5 years ago
Metadata Files
Readme License

README.md

What is lslx?

lslx is a package for fitting semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with elastic-net or minimax concave penalty (MCP) developed by Huang, Chen, and Weng (2017). In this semi-confirmatory method, an SEM model is distinguished into two parts: a confirmatory part and an exploratory part. The confirmatory part includes all of the freely estimated parameters and fixed parameters that are allowed for theory testing. The exploratory part is composed by a set of penalized parameters describing relationships that cannot be clearly determined by available substantive theory. By implementing a sparsity-inducing penalty and choosing an optimal penalty level, the relationships in the exploratory part can be efficiently determined by the sparsity pattern of these penalized parameters.

lslx can be also seen as a package for conducting usual SEM with several state-of-art inference methods, including sandwich standard error formula, mean-adjusted likelihood ratio test, and two-stage method with auxiliary variables for missing data. lslx also supports multi-group analysis for evaluating group heterogeneity and penalized least squares for SEM with ordianl data under delta parameterization. For now, the major limitations of lslx are that (1) it cannot impose linear or non-linear constraints for coefficients; (2) it cannot make valid inference under clustered or dependent data.

To learn more about lslx, please see its JSS paper doi:10.18637/jss.v093.i07.

GitHub Events

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  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 238
  • Total Committers: 5
  • Avg Commits per committer: 47.6
  • Development Distribution Score (DDS): 0.626
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
psyphh p****h@p****l 89
PHHuang p****h@P****l 69
PHHuang p****h@P****l 61
psyphh p****h@g****m 18
Maximilian Bee r****e@f****e 1
Committer Domains (Top 20 + Academic)

Packages

  • Total packages: 1
  • Total downloads:
    • cran 377 last-month
  • Total docker downloads: 20,392
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 12
  • Total maintainers: 1
cran.r-project.org: lslx

Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood or Least Squares

  • Versions: 12
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 377 Last month
  • Docker Downloads: 20,392
Rankings
Forks count: 12.8%
Downloads: 22.0%
Stargazers count: 24.2%
Average: 24.9%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Maintainers (1)
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • R6 * imports
  • Rcpp * imports
  • ggplot2 * imports
  • lavaan * imports
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests