Package: plsmselect
Title: Linear and Smooth Predictor Modelling with Penalisation and
        Variable Selection
Version: 0.2.0
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: 7.0.1
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
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-11-24 09:19:03 UTC; Indrayudh
Author: Indrayudh Ghosal [aut, cre],
  Matthias Kormaksson [aut]
Maintainer: Indrayudh Ghosal <ig248@cornell.edu>
Repository: CRAN
Date/Publication: 2019-11-24 09:50:03 UTC
Built: R 4.6.0; ; 2026-04-26 14:30:19 UTC; unix
