{ "_id": "6a1f2cb7b401979e734261e5", "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). 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), and Q–Q checks of\nresidual z-statistics (resid_qq()), optionally with\nnon-overlapping labels from 'ggrepel' (Slowikowski, 2024\n). Heavy\ncomputations are parallelized via 'future'/'furrr' (Bengtsson,\n2021 ; Vaughan and Dancho, 2018\n). 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", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-15 09:52:07 UTC", "User": "root" }, "Author": "Karel Rečka [aut, cre]", "Maintainer": "Karel Rečka ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-01-17 13:01:55 UTC", "RemoteUrl": "https://github.com/cran/lavDiag", "RemoteRef": "HEAD", "RemoteSha": "aa4c6c8aa48474aae601105280b07e4b62f1a521", "MD5sum": "cdf39da5345add5b0eb9a3dc5f33c31e", "_user": "cran", "_type": "src", "_file": "lavDiag_0.1.0.tar.gz", "_fileid": "ccfb0f93f5ffca7390b4694f28c9f6f69694602e2676958415af420f69cc6cb9", "_filesize": 308507, "_sha256": "ccfb0f93f5ffca7390b4694f28c9f6f69694602e2676958415af420f69cc6cb9", "_created": 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