Generalized Structured Component Analysis Structural Equation Modeling

Implementing generalized structured component analysis (GSCA) and its basic extensions, including constrained single and multiple group analysis, and second order latent variable modeling. For a comprehensive overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754).

Betas-Select in Structural Equation Models and Linear Models

It computes betas-select, coefficients after standardization in structural equation models and regression models, standardizing only selected variables. Supports models with moderation, with product terms formed after standardization. It also offers confidence intervals that account for standardization, including bootstrap confidence intervals as proposed by Cheung et al. (2022) . An introduction to the package can be found in Sun et al. (2026) .

Automatic Calculation of Effects for Piecewise Structural Equation Models

Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 ). Confidence intervals are provided via bootstrapping.

Two Stage Estimation for Generalized Structural Equation Models

Provides a framework to estimate high dimensional generalized structural equation models using two stage quasi-likelihood expectation-maximization. The structural model supports binomial (logit and probit), Poisson, negative binomial, and gamma distributions for the outcome variable. Hattab (2026) "A Two Stage Quasi-Likelihood Estimation Method for High Dimensional Generalized Structural Equation Models" .

Decision Trees with Structural Equation Models Fit in 'Mplus'

Uses recursive partitioning to create homogeneous subgroups based on structural equation models fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.

Complex Partial Least Squares Structural Equation Modeling

Estimate complex Structural Equation Models (SEMs) by fitting Partial Least Squares Structural Equation Modeling (PLS-SEM) and Partial Least Squares consistent Structural Equation Modeling (PLSc-SEM) specifications that handle categorical data, non-linear relations, and multilevel structures. The implementation follows Lohmöller (1989) for the classic PLS-SEM algorithm, Dijkstra and Henseler (2015) for consistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear PLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal PLS-SEM and PLSc-SEM. Additional extensions are under development. The MC-OrdPLSc algorithm, used to handle ordinal interaction models is detailed in Slupphaug et al., (2026). References: Lohmöller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., & Henseler, J. (2015). . "Consistent partial least squares path modeling." Dijkstra, T. K., & Schermelleh-Engel, K. (2014). . "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). . "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026). . "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators."

Evaluation of the Role of Control Variables in Structural Equation Models

Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) .

Genetic-Relationship-Matrix Structural Equation Modelling (GRMSEM)

Quantitative genetics tool supporting the modelling of multivariate genetic variance structures in quantitative data. It allows fitting different models through multivariate genetic-relationship-matrix (GRM) structural equation modelling (SEM) in unrelated individuals, using a maximum likelihood approach. Specifically, it combines genome-wide genotyping information, as captured by GRMs, with twin-research-based SEM techniques, St Pourcain et al. (2017) , Shapland et al. (2020) .

Likelihood-Based Confidence Interval in Structural Equation Models

Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) . Currently implements the algorithm illustrated by Pek and Wu (2018) , and supports the robust LBCI proposed by Falk (2018) .

Manually Plot Path Diagrams for Structural Equation Models

Manually plot fully customisable path diagrams for structural equation models (SEM). Map out node positions using simple coordinates and specify where on the perimeter of each node paths begin and end. Extensive fine-tuning options allow the creation of a path diagram exactly as envisioned, entirely within R.