Package: plsmselect 0.2.0

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).

Authors:Indrayudh Ghosal [aut, cre], Matthias Kormaksson [aut]

plsmselect_0.2.0.tar.gz
plsmselect_0.2.0.zip(r-4.5)plsmselect_0.2.0.zip(r-4.4)plsmselect_0.2.0.zip(r-4.3)
plsmselect_0.2.0.tgz(r-4.4-any)plsmselect_0.2.0.tgz(r-4.3-any)
plsmselect_0.2.0.tar.gz(r-4.5-noble)plsmselect_0.2.0.tar.gz(r-4.4-noble)
plsmselect_0.2.0.tgz(r-4.4-emscripten)plsmselect_0.2.0.tgz(r-4.3-emscripten)
plsmselect.pdf |plsmselect.html✨
plsmselect/json (API)

# Install 'plsmselect' in R:
install.packages('plsmselect', repos = c('https://indrayudhghosal.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • simData - Simulated dataset to be used for gamlasso

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.20 score 16 scripts 155 downloads 4 exports 28 dependencies

Last updated 5 years agofrom:d93f1849b3. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-winOKNov 16 2024
R-4.5-linuxOKNov 16 2024
R-4.4-winOKNov 16 2024
R-4.4-macOKNov 16 2024
R-4.3-winOKNov 16 2024
R-4.3-macOKNov 16 2024

Exports:cumbasehazgamlassogamlassoChecksgamlassoFit

Dependencies:clicodetoolsdplyrfansiforeachgenericsglmnetglueiteratorslatticelifecyclemagrittrMatrixmgcvnlmepillarpkgconfigR6RcppRcppEigenrlangshapesurvivaltibbletidyselectutf8vctrswithr

The plsmselect package

Indrayudh Ghosal and Matthias Kormaksson

Rendered fromplsmselect.Rmdusingknitr::rmarkdownon Nov 16 2024.

Last update: 2019-11-24
Started: 2019-07-19