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]

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On CRAN:

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4 exports 0.00 score 28 dependencies 14 scripts 150 downloads

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

Exports:cumbasehazgamlassogamlassoChecksgamlassoFit

Dependencies:clicodetoolsdplyrfansiforeachgenericsglmnetglueiteratorslatticelifecyclemagrittrMatrixmgcvnlmepillarpkgconfigR6RcppRcppEigenrlangshapesurvivaltibbletidyselectutf8vctrswithr