Perform sensitivity analysis in structural equation modeling using 
    meta-heuristic optimization methods (e.g., ant colony optimization and others). 
    The references for the proposed methods are: 
    (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). 
    <doi:10.1080/10705511.2021.1881786>
    (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017)
    <doi:10.1080/10705511.2018.1506925>;
    (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. 
    (2022). <doi:10.1177/00131644211073121>;
    (4) Socha, K., & Dorigo, M. (2008) <doi:10.1016/j.ejor.2006.06.046>.
    We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization
    algorithm with continuous domains and associated R code,
    which provided the base for the development of this package.

Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.

Provides tools for structural equation modeling, many of which extend the 'lavaan' package; for example, to pool results from multiple imputations, probe latent interactions, or test measurement invariance.

SEM Trees and SEM Forests -- an extension of model-based decision
    trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
    split empirical data into homogeneous groups each sharing similar data patterns
    with respect to a SEM by recursively selecting optimal predictors of these
    differences. SEM forests are an extension of SEM trees. They are ensembles of
    SEM trees each built on a random sample of the original data. By aggregating
    over a forest, we obtain measures of variable importance that are more robust
    than measures from single trees. A description of the method was published by
    Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) <doi:10.1037/a0030001> 
    and Arnold, Voelkle, & Brandmaier (2020) <doi:10.3389/fpsyg.2020.564403>.