Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <doi:10.48550/arXiv.1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.

Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

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).
      <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."