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: 0.2.0
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 <ig248 at>
License: GPL-2
NeedsCompilation: no
CRAN checks: plsmselect results


Reference manual: plsmselect.pdf
Vignettes: The plsmselect package


Package source: plsmselect_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): plsmselect_0.2.0.tgz, r-oldrel (arm64): plsmselect_0.2.0.tgz, r-release (x86_64): plsmselect_0.2.0.tgz, r-oldrel (x86_64): plsmselect_0.2.0.tgz
Old sources: plsmselect archive


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