Package: plsmselect
Title: Linear and Smooth Predictor Modelling with Penalisation and
        Variable Selection
Version: 0.1.3
Authors@R: c(person("Indrayudh", "Ghosal", email = "ig248@cornell.edu", role = c("aut", "cre")),
  person("Matthias", "Kormaksson", email = "matthias.kormaksson@novartis.com", role = "aut"))
Description: 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).
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
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
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-16 19:05:56 UTC; Indrayudh
Author: Indrayudh Ghosal [aut, cre],
  Matthias Kormaksson [aut]
Maintainer: Indrayudh Ghosal <ig248@cornell.edu>
Repository: CRAN
Date/Publication: 2019-07-19 08:40:02 UTC
