Bootstrap Hyperparameter Selection for PLS Models and Extensions
bootPLS: Bootstrap Hyperparameter Selection for PLS Models and Extensi...
Bootstrap (Y,X) for the coefficients with number of components updated...
Bootstrap (Y,T) functions for PLSR
Bootstrap (Y,T) function for PLSGLR
Bootstrap (Y,T) function for plsRglm
Internal bigPLS functions
Non-parametric (Y,T) Bootstrap for selecting the number of components ...
Non-parametric (Y,T) Bootstrap for selecting the number of components ...
Number of components for SGPLS using (Y,T) bootstrap (parallel version...
Number of components for SGPLS using (Y,T) bootstrap
Title
Title
Permutation bootstrap (Y,T) function for PLSR
Permutation bootstrap (Y,T) function for PLSGLR
Permutation Bootstrap (Y,T) function for plsRglm
Graphical assessment of the stability of selected variables
Data generating function for univariate gamma plsR models
Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).
Useful links