{"id":16178393,"url":"https://github.com/sfcheung/semfindr","last_synced_at":"2026-02-24T17:40:59.423Z","repository":{"id":65082669,"uuid":"271692994","full_name":"sfcheung/semfindr","owner":"sfcheung","description":"A find(e)r of influential cases and outliers in SEM","archived":false,"fork":false,"pushed_at":"2025-03-23T09:41:18.000Z","size":153337,"stargazers_count":1,"open_issues_count":4,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-30T01:51:47.755Z","etag":null,"topics":["diagnostics","influential-cases","lavaan","outlier-detection","r","r-package","sensitivity-analysis","structural-equation-modeling"],"latest_commit_sha":null,"homepage":"https://sfcheung.github.io/semfindr/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sfcheung.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-12T02:44:37.000Z","updated_at":"2025-03-03T19:16:33.000Z","dependencies_parsed_at":"2023-02-06T05:46:16.131Z","dependency_job_id":"88e3ded0-230d-4b93-802b-20af281b5393","html_url":"https://github.com/sfcheung/semfindr","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfcheung%2Fsemfindr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfcheung%2Fsemfindr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfcheung%2Fsemfindr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfcheung%2Fsemfindr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sfcheung","download_url":"https://codeload.github.com/sfcheung/semfindr/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250479930,"owners_count":21437458,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["diagnostics","influential-cases","lavaan","outlier-detection","r","r-package","sensitivity-analysis","structural-equation-modeling"],"created_at":"2024-10-10T05:13:39.676Z","updated_at":"2026-02-24T17:40:59.373Z","avatar_url":"https://github.com/sfcheung.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!-- badges: start --\u003e\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![CRAN status](https://www.r-pkg.org/badges/version/semfindr?color=blue)](https://CRAN.R-project.org/package=semfindr)\n[![CRAN: Release Date](https://www.r-pkg.org/badges/last-release/semfindr?color=blue)](https://cran.r-project.org/package=semfindr)\n[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/semfindr?color=blue)](https://r-pkg.org/pkg/semfindr)\n[![Code size](https://img.shields.io/github/languages/code-size/sfcheung/semfindr.svg)](https://github.com/sfcheung/semfindr)\n[![Last Commit at Master](https://img.shields.io/github/last-commit/sfcheung/semfindr.svg)](https://github.com/sfcheung/semfindr/commits/master)\n[![R-CMD-check](https://github.com/sfcheung/semfindr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sfcheung/semfindr/actions/workflows/R-CMD-check.yaml)\n\u003c!-- badges: end --\u003e\n\n(Version 0.1.9, updated on 2025-03-04, [release history](https://sfcheung.github.io/semfindr/news/index.html))\n\n# semfindr: Finding influential cases in SEM \u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"150\" /\u003e\n\nA find(e)r of influential cases in structural equation modeling\nbased mainly on the sensitivity analysis procedures presented by Pek and\nMacCallum (2011).\n\nThis package supports two approaches: leave-one-out analysis and approximate\ncase influence.\n\n## Leave-One-Out Analysis\n\nThis approach examines the influence of each case by refitting a model with\nthis case removed.\n\nUnlike other similar\npackages, the workflow adopted in semfindr separates the leave-one-out\nanalysis (refitting a model with one case removed) from the case influence\nmeasures.\n\n- Users first do the leave-one-out model fitting for all cases, or\ncases selected based on some criteria\n(`vignette(\"selecting_cases\", package = \"semfindr\")`), using\n`lavaan_rerun()`.\n\n- Users then compute case influence measures\nusing the output of `lavaan_rerun()`.\n\nThis approaches avoids unnecessarily refitting the models for each set of\ninfluence measures, and also allows analyzing only probable influential cases\nwhen the model takes a long time to fit.\n\nThe functions were designed to be flexible\nsuch that users can compute case influence measures such as\n\n- standardized parameter estimates and generalized Cook's distance for\n  selected parameters;\n- changes in raw or standardized estimates of parameters;\n- changes in fit measures supported by `lavaan::fitMeasures()`.\n\nThis package can also be generate plots to visualize\ncase influence, including a bubble plot similar to that by `car::influencePlot()`\nAll plots generated are `ggplot` plots that can be further modified by users.\nMore can be found in *Quick Start* (`vignette(\"semfindr\", package = \"semfindr\")`).\n\n## Approximate Case Influence\n\nThis approach computes the approximate influence of each case using *casewise*\n*scores* and *casewise* *likelihood*. This method is efficient because it does\nnot requires refitting the model for each case. However, it can only approximate\nthe influence, unlike the leave-one-out approach, which produce exact influence.\nThis approach can be used when the number of cases is very large\nand/or the model takes a long time to fit. Technical details can be found in the\nvignette *Approximate Case Influence Using Scores and Casewise Likelihood*\n(`vignette(\"casewise_scores\", package = \"semfindr\")`).\n\n# Installation\n\nThe stable version at CRAN can be installed by `install.packages()`:\n\n```r\ninstall.packages(\"semfindr\")\n```\n\nThe latest developmental version can be installed by `remotes::install_github`:\n\n```r\nremotes::install_github(\"sfcheung/semfindr\")\n```\n\nYou can learn more about this package at the\n[Github page](https://sfcheung.github.io/semfindr/) of this\npackage and\nQuick Start (`vignette(\"semfindr\", package = \"semfindr\")`).\n\n# Reference\n\nPek, J., \u0026 MacCallum, R. (2011). Sensitivity analysis in structural equation\nmodels: Cases and their influence. *Multivariate Behavioral Research, 46*(2),\n202-228. https://doi.org/10.1080/00273171.2011.561068\n\n# Comments, Suggestions, and Bug Reports\n\nPlease post your comments, suggestions, and bug reports as issues\nat [GitHub](https://github.com/sfcheung/semptools/issues), or contact\nthe maintainer by email. Thanks in advance for trying out `semfindr`.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsfcheung%2Fsemfindr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsfcheung%2Fsemfindr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsfcheung%2Fsemfindr/lists"}