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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. References: Lohmöller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., &amp; Henseler, J. (2015). &lt;<a href="https://doi.org/10.1016%2Fj.jmva.2015.06.002" target="_top">doi:10.1016/j.jmva.2015.06.002</a>&gt;. "Consistent partial least squares path modeling." Dijkstra, T. K., &amp; Schermelleh-Engel, K. (2014). &lt;<a href="https://doi.org/10.1016%2Fj.csda.2014.07.008" target="_top">doi:10.1016/j.csda.2014.07.008</a>&gt;. "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., &amp; Dijkstra, T. K. (2018). &lt;<a href="https://doi.org/10.1007%2Fs11135-018-0767-9" target="_top">doi:10.1007/s11135-018-0767-9</a>&gt;. "Partial least squares path modeling using ordinal categorical indicators."

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NCA software is a comprehensive but straightforward tool for a complete evaluation of data.