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 Lohmller (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: Lohmller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., Henseler, J. (2015). doi:10.1016/j.jmva.2015.06.002. "Consistent partial least squares path modeling." Dijkstra, T. K., Schermelleh-Engel, K. (2014). doi:10.1016/j.csda.2014.07.008. "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., Dijkstra, T. K. (2018). doi:10.1007/s11135-018-0767-9. "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. Mittner, M. (2026). doi:10.31234/osf.io/fwzj6_v1. "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators."