{
  "_id": "6a1f2d10b401979e734262fe",
  "Package": "lavDiag",
  "Title": "Latent Variable Models Diagnostics",
  "Version": "0.1.0",
  "Authors@R": "person(\"Karel\", \"Rečka\", , \"reckak@mail.muni.cz\", role = c(\"aut\", \"cre\"))",
  "Description": "Diagnostics and visualization tools for latent variable\nmodels fitted with 'lavaan' (Rosseel, 2012\n<doi:10.18637/jss.v048.i02>). The package provides fast,\nparallel-safe factor-score prediction (lavPredict_parallel()),\ndata augmentation with model predictions, residuals,\ndelta-method standard errors and confidence intervals\n(augment()), and model-based latent grids for continuous,\nordinal, or mixed indicators (prepare()). It offers item-level\nempirical versus model curve comparison using generalized\nadditive models for both continuous and ordinal indicators\n(item_data(), item_plot()) via 'mgcv' (Wood, 2017,\nISBN:9781498728331), residual diagnostics including residual\ncorrelation tables and plots (resid_cor(), resid_corrplot())\nusing 'corrplot' (Wei and Simko, 2021\n<https://github.com/taiyun/corrplot>), and Q–Q checks of\nresidual z-statistics (resid_qq()), optionally with\nnon-overlapping labels from 'ggrepel' (Slowikowski, 2024\n<https://CRAN.R-project.org/package=ggrepel>). Heavy\ncomputations are parallelized via 'future'/'furrr' (Bengtsson,\n2021 <doi:10.32614/RJ-2021-048>; Vaughan and Dancho, 2018\n<https://CRAN.R-project.org/package=furrr>). Methods build on\nestablished literature and packages listed above.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/reckak/lavDiag",
  "BugReports": "https://github.com/reckak/lavDiag/issues",
  "Encoding": "UTF-8",
  "Language": "en-US",
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  "Packaged": {
    "Date": "2026-05-15 09:50:08 UTC",
    "User": "root"
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  "Author": "Karel Rečka [aut, cre]",
  "Maintainer": "Karel Rečka <reckak@mail.muni.cz>",
  "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libuv1-dev\nlibxml2-dev",
  "Repository": "https://cranhaven.r-universe.dev",
  "Date/Publication": "2026-05-15 02:02:00 UTC",
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    "packages",
    "r-universe"
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    "extra/citation.json",
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    "hopper_plot",
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    "item_plot",
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      "page": "augment",
      "title": "Augment SEM data with predictions, residuals, SEs/CIs, and ordinal extras",
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        "augment"
      ]
    },
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        "lavDiag-visualization"
      ],
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        "hopper_plot"
      ]
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      ]
    },
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      "title": "Plot model-implied vs empirical item curves by latent factor (single- or multi-group)",
      "concept": [
        "lavDiag-visualization"
      ],
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        "item_plot"
      ]
    },
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      "title": "Fast & robust parallel wrapper for lavaan::lavPredict()",
      "topics": [
        "lavPredict_parallel"
      ]
    },
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      "title": "Extract compact, robust meta-information about a lavaan fit",
      "topics": [
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      ]
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      ]
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