semlbci

Find the likelihood based confidence intervals for parameters in structural equation modeling

https://github.com/sfcheung/semlbci

Science Score: 26.0%

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Keywords

confidence-intervals lavaan likelihood-based profile-likelihood r r-package structural-equation-modeling
Last synced: 6 months ago · JSON representation

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Find the likelihood based confidence intervals for parameters in structural equation modeling

Basic Info
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  • Stars: 1
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  • Open Issues: 1
  • Releases: 10
Topics
confidence-intervals lavaan likelihood-based profile-likelihood r r-package structural-equation-modeling
Created over 5 years ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

Lifecycle: stable Project Status: Active - The project has reached a stable, usable state and is being actively developed. CRAN status CRAN: Release Date Code size Last Commit at Main R-CMD-check DOI <!-- badges: end -->

(Version 0.11.3, updated on 2025-01-25 release history)

semlbci

This package includes functions for forming the likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling. It also supports the robust LBCI proposed by Falk (2018). It was described in the following manuscript:

  • Cheung, S. F., & Pesigan, I. J. A. (2023). semlbci: An R package for forming likelihood-based confidence intervals for parameter estimates, correlations, indirect effects, and other derived parameters. Structural Equation Modeling: A Multidisciplinary Journal. 30(6), 985--999. https://doi.org/10.1080/10705511.2023.2183860

As argued in the article and by others, LBCI is usually better than Wald-based confidence interval and delta method confidence interval, which are the default method in most structural equation modeling (SEM) program. However, there is one technical disadvantage: LBCI cannot be directly computed but needs to be "found" (searched) by some algorithms. Wald CIs, on the other hand, can be computed quickly.

In semlbci, we try to address this disadvantage of LBCI by implementing an efficient method (illustrated by Pek & Wu, 2018, adapted from Wu & Neale, 2012), to help researchers to form LBCIs for model parameters, including user-defined parameters, in models fitted by lavaan. It can also form LBCIs for the standardized solution, such as "betas" (standardized regression coefficients) and correlations, and support multiple-group models. Last, it supports the robust LBCI proposed by Falk (2018) for nonnormal variables.

More information on this package can be found below:

https://sfcheung.github.io/semlbci/

How To Use It

Illustration with examples can be found in the Get Started guide (vignette("semlbci", package = "semlbci")).

Installation

The stable CRAN version can be installed by install.packages():

r install.packages("semlbci")

The latest version at GitHub can be installed by remotes::install_github():

r remotes::install_github("sfcheung/semlbci")

Implementation

It currently implements the algorithm illustrated by Pek and Wu (2018), adapted from Wu and Neale (2012) without adjustment for parameters with attainable bounds. It also supports the robust LBCI proposed by Falk (2018). More on the implementation can be found in the technical appendices.

References

Cheung, S. F., & Pesigan, I. J. A. (2023). semlbci: An R package for forming likelihood-based confidence intervals for parameter estimates, correlations, indirect effects, and other derived parameters. Structural Equation Modeling: A Multidisciplinary Journal. 30(6), 985--999. https://doi.org/10.1080/10705511.2023.2183860

Falk, C. F. (2018). Are robust standard errors the best approach for interval estimation with nonnormal data in structural equation modeling? Structural Equation Modeling: A Multidisciplinary Journal, 25(2), 244-266. https://doi.org/10.1080/10705511.2017.1367254

Pek, J., & Wu, H. (2015). Profile likelihood-based confidence intervals and regions for structural equation models. Psychometrika, 80(4), 1123-1145. https://doi.org/10.1007/s11336-015-9461-1

Wu, H., & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior Genetics, 42(6), 886-898. https://doi.org/10.1007/s10519-012-9560-z

Issues

If you have any suggestions or found any bugs or limitations, please feel feel to open a GitHub issue. Thanks.

https://github.com/sfcheung/semlbci/issues

Owner

  • Name: Shu Fai Cheung
  • Login: sfcheung
  • Kind: user
  • Location: Macao
  • Company: University of Macau

GitHub Events

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  • Release event: 1
  • Push event: 6
  • Pull request event: 1
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Last Year
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  • Push event: 6
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Last synced: 11 months ago

All Time
  • Total Commits: 938
  • Total Committers: 2
  • Avg Commits per committer: 469.0
  • Development Distribution Score (DDS): 0.001
Past Year
  • Commits: 86
  • Committers: 1
  • Avg Commits per committer: 86.0
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Top Committers
Name Email Commits
Shu Fai Cheung s****g@g****m 937
Ivan Jacob Agaloos Pesigan j****b 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 60
  • Total pull requests: 56
  • Average time to close issues: 6 months
  • Average time to close pull requests: 7 minutes
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 0.32
  • Average comments per pull request: 0.05
  • Merged pull requests: 54
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: about 6 hours
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • sfcheung (59)
  • yrosseel (1)
Pull Request Authors
  • sfcheung (58)
  • jeksterslab (1)
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enhancement (38) low priority (13) top priority (7) bug (6) project management (4) documentation (4) speed (4) test (2) maintanability (1) clean up (1)
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  • Total versions: 4
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cran.r-project.org: semlbci

Likelihood-Based Confidence Interval in Structural Equation Models

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 159 Last month
  • Docker Downloads: 1,473
Rankings
Forks count: 28.3%
Dependent packages count: 28.3%
Stargazers count: 31.3%
Dependent repos count: 36.9%
Average: 37.5%
Downloads: 62.6%
Maintainers (1)
Last synced: 6 months ago