Building on the infrastructure provided by the lattice
	     package, this package provides several new high-level
	     functions and methods, as well as additional utilities
	     such as panel and axis annotation functions.

A general implementation of Structural Equation Models
	with latent variables (MLE, 2SLS, and composite likelihood
	estimators) with both continuous, censored, and ordinal
	outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
	Mixture latent variable models and non-linear latent variable models
	(Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>).
	The package also provides methods for graph exploration (d-separation,
	back-door criterion), simulation of general non-linear latent variable
	models, and estimation of influence functions for a broad range of
	statistical models.

Fit a variety of latent variable models, including confirmatory
   factor analysis, structural equation modeling and latent growth curve models.

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.

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 variables 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: 10.31234/osf.io/f4ary> contains a tutorial.