Linear and Smooth Predictor Modelling with Penalisation and Variable Selection

Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).


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install.packages("plsmselect")

0.1.3 by Indrayudh Ghosal, a month ago


Browse source code at https://github.com/cran/plsmselect


Authors: Indrayudh Ghosal [aut, cre] , Matthias Kormaksson [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports dplyr, glmnet, mgcv, survival

Suggests tidyverse, knitr, rmarkdown, kableExtra


See at CRAN