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.

Companion toolbox for structural equation models fitted with 'lavaan'. Provides post-estimation diagnostics and graphics that operate directly on a fitted object using its estimates and covariance, and refits auxiliary models when needed. The package relies on 'lavaan' (Rosseel, 2012) <doi:10.18637/jss.v048.i02>.

A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.