plssem-package: plssem

In plssem: Complex Partial Least Squares Structural Equation Modeling

plssem

Description

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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmva.2015.06.002")}. "Consistent partial least squares path modeling." Dijkstra, T. K., & Schermelleh-Engel, K. (2014). \Sexpr[results=rd]{tools:::Rd_expr_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). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11135-018-0767-9")}. "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026). \Sexpr[results=rd]{tools:::Rd_expr_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."

Author(s)

Maintainer: Kjell Solem Slupphaug [e-mail] (ORCID)

Authors:

• Kjell Solem Slupphaug [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/kss2k/plssem

• https://kss2k.github.io/plssem/

plssem documentation built on Sept. 26, 2026, 5:06 p.m.