lavDiag - Latent Variable Models Diagnostics
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.
