plsRglm, PLS generalized linear models for the R language.
Résumé
There are mainly two aims for the plsRglm library written by the authors for the R software. The extension of PLS regression to generalized linear models, and for instance to logistic regression models, and the need to provide tools to PLS users to deal with incomplete datasets using cross-validation. These models were successfully applied to datasets of various kinds: to multiple regression problems linked to the famous mixture dataset of Cornell, to generalized linear regression and especially to a study of Bordeaux wine quality thanks to an ordinal logistic regression model. More recently, the authors carried out the analysis of allelotyping data thanks to PLS binary logistic regression models in order to enhance the understanding of mechanisms involved in the evolution of cancers.