plsmselect: Linear and Smooth Predictor Modelling with Penalisation and Variable Selection (original) (raw)
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).
| Version: | 0.2.0 |
|---|---|
| Depends: | R (≥ 3.5.0) |
| Imports: | dplyr (≥ 0.7.8), glmnet (≥ 2.0.16), mgcv (≥ 1.8.26), survival (≥ 2.43.3) |
| Suggests: | knitr, rmarkdown, kableExtra, purrr |
| Published: | 2019-11-24 |
| DOI: | 10.32614/CRAN.package.plsmselect |
| Author: | Indrayudh Ghosal [aut, cre], Matthias Kormaksson [aut] |
| Maintainer: | Indrayudh Ghosal |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | plsmselect results |
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