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plsmselect  

Linear and Smooth Predictor Modelling with Penalisation and Variable Selection
View on CRAN: Click here


Download and install plsmselect package within the R console
Install from CRAN:
install.packages("plsmselect")

Install from Github:
library("remotes")
install_github("cran/plsmselect")

Install by package version:
library("remotes")
install_version("plsmselect", "0.2.0")



Attach the package and use:
library("plsmselect")
Maintained by
Indrayudh Ghosal
[Scholar Profile | Author Map]
First Published: 2019-07-19
Latest Update: 2019-11-24
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).
How to cite:
Indrayudh Ghosal (2019). plsmselect: Linear and Smooth Predictor Modelling with Penalisation and Variable Selection. R package version 0.2.0, https://cran.r-project.org/web/packages/plsmselect. Accessed 06 Apr. 2025.
Previous versions and publish date:
0.1.3 (2019-07-19 10:40)
Other packages that cited plsmselect R package
View plsmselect citation profile
Other R packages that plsmselect depends, imports, suggests or enhances
Complete documentation for plsmselect
Functions, R codes and Examples using the plsmselect R package
Some associated functions: cbh . create_dataset . cumbasehaz . find_family . formula_setup . gamlasso . gamlassoChecks . gamlassoFit . lasso_gam_loop . meandist . nzeros . predict.gamlasso . print.gamlasso . readconfirm . simData . summary.gamlasso . 
Some associated R codes: check.R . cumbasehaz.R . data.R . fit.R . internal.R . output.R .  Full plsmselect package functions and examples
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