This package provides Partial least squares Regression for
(weighted) generalized linear models and kfold crossvalidation
of such models using various criteria. It allows for missing
data in the eXplanatory variables. Bootstrap confidence
intervals constructions are also available.

This package provides Partial least squares Regression for (weighted) generalized linear models and kfold crossvalidation of such models using various criteria. It allows for missing data in the eXplanatory variables. Bootstrap confidence intervals constructions are also available.

This package 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.

MASS, plsdof, R.rsp, chemometrics, plsdepot
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.

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

Variable Selection in Partial Least Squares
Kristian Hovde Liland, Tahir Mehmood, Solve Sæbø
Interfaces and methods for variable selection in Partial Least
Squares. The methods include filter methods, wrapper methods and embedded
methods.
grDevices, graphics, genalg, mvtnorm, bdsmatrix, pls, MASS,
progress, parallel, stats

grDevices, graphics, genalg, mvtnorm, bdsmatrix, MASS,
progress, parallel, stats

Interfaces and methods for variable selection in Partial Least
Squares. The methods include filter methods, wrapper methods and embedded
methods. Both regression and classification is supported.