Publication details
lavDiag: Latent Variable Models Diagnostics
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Software |
| MU Faculty or unit | |
| web | https://cran.r-project.org/web/packages/lavDiag/index.html |
| Description | Diagnostics and visualization tools for latent variable models fitted with 'lavaan' (Rosseel, 2012 ). 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 ), and Q–Q checks of residual z-statistics (resid_qq()), optionally with non-overlapping labels from 'ggrepel' (Slowikowski, 2024 ). Heavy computations are parallelized via 'future'/'furrr' (Bengtsson, 2021 ; Vaughan and Dancho, 2018 ). Methods build on established literature and packages listed above. |
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