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.

Affords an alternative, vector-based syntax to 'lavaan', as well as other 
             convenience functions such as naming paths and defining indirect
             links automatically, in addition to convenience formatting optimized
             for a publication and script sharing workflow.

Provides a graphical user interface with an 
    integrated diagrammer for latent variable models from the 'lavaan' package. 
    It offers two core functions:  first, lavaangui() launches a web application
    that allows users to specify models by drawing path diagrams, fitting them, 
    assessing model fit, and more; second, plot_lavaan() creates 
    interactive path diagrams from models specified in 'lavaan'. 
    Karch (2024) <doi: 10.1080/10705511.2024.2420678> contains a tutorial.

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.

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.