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  "Type": "Package",
  "Title": "Automate Latent Growth Mixture Modelling in 'Mplus'",
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  "Authors@R": "c(\nperson(\"Olivier\", \"Percie du Sert\", email = \"olivier.perciedusert@mail.mcgill.ca\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0002-6283-2529\")),\nperson(\"Joshua\", \"Unrau\", email = \"Joshua.unrau@mail.mcgill.ca\", role = \"aut\"))",
  "Description": "Provide a suite of functions for conducting and automating\nLatent Growth Modeling (LGM) in 'Mplus', including Growth Curve\nModel (GCM), Growth-Based Trajectory Model (GBTM), Latent Class\nGrowth Analysis (LCGA) and Growth Mixture Model (GMM). The\npackage builds upon the capabilities of the 'MplusAutomation'\npackage (Hallquist & Wiley, 2018) to streamline large-scale\nlatent variable analyses. “MplusAutomation: An R Package for\nFacilitating Large-Scale Latent Variable Analyses in Mplus.”\nStructural Equation Modeling, 25(4), 621–638.\n<doi:10.1080/10705511.2017.1402334> The workflow implemented in\nthis package follows the recommendations outlined in Van Der\nNest et al. (2020). “An Overview of Mixture Modeling for Latent\nEvolutions in Longitudinal Data: Modeling Approaches, Fit\nStatistics, and Software.” Advances in Life Course Research,\n43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "URL": "https://github.com/OlivierPDS/MplusLGM",
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  "Repository": "https://olivierpds.r-universe.dev",
  "Date/Publication": "2025-07-22 17:59:04 UTC",
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    "User": "root"
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  "Author": "Olivier Percie du Sert [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-6283-2529>),\nJoshua Unrau [aut]",
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  "_created": "2026-10-08T09:31:48.000Z",
  "_published": "2026-10-08T10:19:21.166Z",
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