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.

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.

The primary purpose of 'lavaan.mi' is to extend the functionality 
     of the R package 'lavaan', which implements structural equation modeling
     (SEM).  When incomplete data have been multiply imputed, the imputed data
     sets can be analyzed by 'lavaan' using complete-data estimation methods,
     but results must be pooled across imputations (Rubin, 1987, <doi:10.1002/9780470316696>).
     The 'lavaan.mi' package automates the pooling of point and standard-error
     estimates, as well as a variety of test statistics, using a familiar interface
     that allows users to fit an SEM to multiple imputations as they would to a
     single data set using the 'lavaan' package.