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  "Title": "Shiny Application for Latent Structure Analysis with a Graphical\nUser Interface",
  "Version": "0.0.9",
  "Authors@R": "c(\nperson(\ngiven = \"Hasan\",\nfamily = \"Djidu\",\nemail = \"hasandjidu@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-1110-6815\")\n),\nperson(\ngiven = \"Heri\",\nfamily = \"Retnawati\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0002-1792-5873\")\n),\nperson(\ngiven = \"Samsul\",\nfamily = \"Hadi\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0003-3437-2542\")\n),\nperson(\ngiven = \"Haryanto\",\nfamily = \"\",\nrole = \"ctb\",\ncomment = c(ORCID = \"0000-0003-3322-904X\")\n)\n)",
  "Description": "Provides an interactive Shiny-based toolkit for conducting\nlatent structure analyses, including Latent Profile Analysis\n(LPA), Latent Class Analysis (LCA), Latent Trait Analysis\n(LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory\nFactor Analysis (CFA), and Structural Equation Modeling (SEM).\nThe implementation is grounded in established methodological\nframeworks: LPA is supported through 'tidyLPA' (Rosenberg et\nal., 2018) <doi:10.21105/joss.00978>, LCA through 'poLCA'\n(Linzer & Lewis, 2011) <doi:10.32614/CRAN.package.poLCA> &\n'glca' (Kim & Kim, 2024) <doi:10.32614/CRAN.package.glca>,\nLTA/IRT via 'mirt' (Chalmers, 2012)\n<doi:10.18637/jss.v048.i06>, and EFA via 'psych' (Revelle,\n2025). SEM and CFA functionalities build upon the 'lavaan'\nframework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. Users\ncan upload datasets or use built-in examples, fit models,\ncompare fit indices, visualize results, and export outputs\nwithout programming.",
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  "Repository": "https://hdmeasure.r-universe.dev",
  "Date/Publication": "2026-05-09 10:43:37 UTC",
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