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 models
    fitted with 'lavaan' (Rosseel, 2012 <doi:10.18637/jss.v048.i02>).
    The package provides fast, parallel-safe factor-score prediction
    (lavPredict_parallel()), data augmentation with model predictions,
    residuals, delta-method standard errors and confidence intervals
    (augment()), and model-based latent grids for continuous, ordinal, or
    mixed indicators (prepare()). It offers item-level empirical versus
    model curve comparison using generalized additive models for both
    continuous and ordinal indicators (item_data(), item_plot()) via 'mgcv'
    (Wood, 2017, ISBN:9781498728331), residual diagnostics including
    residual correlation tables and plots (resid_cor(), resid_corrplot())
    using 'corrplot' (Wei and Simko, 2021 <https://github.com/taiyun/corrplot>),
    and Q–Q checks of residual z-statistics (resid_qq()), optionally with
    non-overlapping labels from 'ggrepel' (Slowikowski, 2024
    <https://CRAN.R-project.org/package=ggrepel>). Heavy computations are 
    parallelized via 'future'/'furrr' (Bengtsson, 2021 <doi:10.32614/RJ-2021-048>;
    Vaughan and Dancho, 2018 <https://CRAN.R-project.org/package=furrr>).
    Methods build on established 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
Depends: R (>= 4.1.0)
Imports: corrplot (>= 0.90), dplyr, furrr, future, future.apply,
        ggplot2, ggrepel, igraph, lavaan, mgcv, purrr, rlang, stringr,
        tibble, tidyselect, tidyr, vctrs, visNetwork, withr
Suggests: spelling, testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-12-28 18:11:32 UTC; ddrea
Author: Karel Rečka [aut, cre]
Maintainer: Karel Rečka <reckak@mail.muni.cz>
Repository: CRAN
Date/Publication: 2026-01-17 11:50:08 UTC
Built: R 4.5.2; ; 2026-01-23 04:03:19 UTC; windows
