Performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation.

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.

Computes robust p-values for overall fit and nested comparisons of
    structural equation models fitted with 'lavaan'. Implements penalized
    eigenvalue block averaging and penalized regression (Foldnes, Moss,
    Grønneberg, 2025) <doi:10.1080/10705511.2024.2372028>, including their
    extension to nested models (Foldnes, Grønneberg, Moss, 2026)
    <doi:10.3758/s13428-026-02968-4>, alongside familiar corrections such as
    Satorra-Bentler. Supported settings include complete-data ML, GLS, and ULS,
    categorical DWLS and ULS, and full-information maximum likelihood with one
    or several groups.

Provides miscellaneous tools for structural equation modeling, 
             many of which extend the 'lavaan' package. For example, latent
             interactions can be estimated using product indicators (Lin et al.,
             2010, <doi:10.1080/10705511.2010.488999>) and simple effects probed;
             analytical power analyses can be conducted (Jak et al., 2021,
             <doi:10.3758/s13428-020-01479-0>); and scale reliability
             can be estimated based on estimated factor-model parameters.

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>.