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).

Version: 0.1.3
Depends: R (≥ 3.5.0)
Imports: dplyr (≥ 0.7.8), glmnet (≥ 2.0.16), mgcv (≥ 1.8.26), survival (≥ 2.43.3)
Suggests: tidyverse (≥ 1.2.1), knitr, rmarkdown, kableExtra
Published: 2019-07-19
Author: Indrayudh Ghosal [aut, cre], Matthias Kormaksson [aut]
Maintainer: Indrayudh Ghosal <ig248 at cornell.edu>
License: GPL-2
NeedsCompilation: no
CRAN checks: plsmselect results

Downloads:

Reference manual: plsmselect.pdf
Vignettes: The plsmselect package
Package source: plsmselect_0.1.3.tar.gz
Windows binaries: r-devel: plsmselect_0.1.3.zip, r-devel-gcc8: plsmselect_0.1.3.zip, r-release: plsmselect_0.1.3.zip, r-oldrel: plsmselect_0.1.3.zip
OS X binaries: r-release: plsmselect_0.1.3.tgz, r-oldrel: plsmselect_0.1.3.tgz

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