plsRglm: Partial Least Squares Regression for Generalized Linear Models. Provides (weighted) Partial least squares Regression for generalized linear models and repeated 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.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Nengsih, Titin Agustin; Bertrand, Frédéric; Maumy-Bertrand, Myriam; Meyer, Nicolas: Determining the number of components in PLS regression on incomplete data set (2019)
- Magnanensi, Jérémy; Bertrand, Frédéric; Maumy-Bertrand, Myriam; Meyer, Nicolas: A new universal resample-stable bootstrap-based stopping criterion for PLS component construction (2017)
- Magnanensi, Jérémy; Maumy-Bertrand, Myriam; Meyer, Nicolas; Bertrand, Frédéric: A new bootstrap-based stopping criterion in PLS components construction (2016)
- Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)