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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0.2.0 by Indrayudh Ghosal, a year ago

Browse source code at

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

Documentation:   PDF Manual  

GPL-2 license

Imports dplyr, glmnet, mgcv, survival

Suggests knitr, rmarkdown, kableExtra, purrr

See at CRAN