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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).
| Version: | 0.2.0 |
| 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 |
| Published: | 2019-11-24 |
| Author: | Indrayudh Ghosal [aut, cre], Matthias Kormaksson [aut] |
| Maintainer: | Indrayudh Ghosal <ig248 at cornell.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | plsmselect results |
| Reference manual: | plsmselect.pdf |
| Vignettes: |
The plsmselect package |
| Package source: | plsmselect_0.2.0.tar.gz |
| Windows binaries: | r-devel: plsmselect_0.2.0.zip, r-release: plsmselect_0.2.0.zip, r-oldrel: plsmselect_0.2.0.zip |
| macOS binaries: | r-release (arm64): plsmselect_0.2.0.tgz, r-oldrel (arm64): plsmselect_0.2.0.tgz, r-release (x86_64): plsmselect_0.2.0.tgz, r-oldrel (x86_64): plsmselect_0.2.0.tgz |
| Old sources: | plsmselect archive |
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These binaries (installable software) and packages are in development.
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