Plots path diagrams from models in 'lavaan' using the plotting
    functionality from the 'DiagrammeR' package. 'DiagrammeR' provides nice path diagrams 
    via 'Graphviz', and these functions make it easy to generate these diagrams from a
    'lavaan' path model without having to write the DOT language graph specification.

Helpers for customizing selected outputs from
  'lavaan' by Rosseel (2012) <doi:10.18637/jss.v048.i02>
  and print them. The functions are intended to be used
  by package developers in their packages and so are not
  designed to be user-friendly. They are designed to be
  let developers customize the tables by other
  functions. Currently the parameter estimates tables of a
  fitted object are supported.

Interactive shiny application for working with different kinds of
    latent variable analysis, with the 'lavaan' package. Graphical output for models
    are provided and different estimators are supported.

Estimation of a multi-group count regression models (i.e., Poisson, 
    negative binomial) with latent covariates. This packages provides two extensions
    compared to ordinary count regression models based on a generalized linear model:
    First, measurement models for the predictors can be specified allowing to account 
    for measurement error. Second, the count regression can be simultaneously estimated 
    in multiple groups with stochastic group weights. The marginal maximum likelihood 
    estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.

Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.

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.