Provides routines to check identifiability of linear structural 
    equation models and factor analysis models. The routines are based on the 
    graphical representation of structural equation models.

Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable.
    Test of independence and feature screening for classification problems can be implemented via the two dependence measures.
    For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>;
    Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>;
    Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.

Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.

A powerful, easy to use syntax for specifying and estimating complex 
    Structural Equation Models. Models can be estimated using Partial 
    Least Squares Path Modeling or Covariance-Based Structural Equation 
    Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 <doi:10.2139/ssrn.3900621>).

Supplementary tools for evaluating and validating partial least
    squares structural equation models estimated with 'seminr'. Provides methods
    for predictive model assessment, importance-performance analysis with
    necessary condition testing, overfitting diagnostics, measurement model
    verification, mediator contribution analysis, unobserved heterogeneity
    detection via latent class and prediction-oriented segmentation, and
    congruence coefficient testing. All functions accept estimated 'seminr'
    model objects and return results with print, summary, and plot methods.