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>.

Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<arXiv:2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix.

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 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. Methods described in Ray, Danks, and Valdez (2021).

Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.

Copula based Cox proportional hazards models for survival data subject to dependent 
    censoring. This approach does not assume that the parameter defining the copula is known. The 
    dependency parameter is estimated with other finite model parameters by maximizing a Pseudo 
    likelihood function. The cumulative hazard function is estimated via estimating equations 
    derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal
    copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any
    parametric model that satisfies certain identifiability conditions could be used. Implemented 
    methods are described in the article "Copula based Cox proportional hazards models for dependent
    censoring" by Deresa and Van Keilegom (2023) <doi:10.1080/01621459.2022.2161387>.