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plsmselect: 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).
Getting started
Browse package contents
Package details |
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| Author | Indrayudh Ghosal [aut, cre], Matthias Kormaksson [aut] |
| Maintainer | Indrayudh Ghosal <ig248@cornell.edu> |
| License | GPL-2 |
| Version | 0.2.0 |
| Package repository | View on CRAN |
| Installation |
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