penalized: L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation in GLMs and in the Cox Model (original) (raw)
Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.
| Version: | 0.9-52 |
|---|---|
| Depends: | R (≥ 2.10.0), survival, methods |
| Imports: | Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | globaltest |
| Published: | 2022-04-23 |
| DOI: | 10.32614/CRAN.package.penalized |
| Author: | Jelle Goeman, Rosa Meijer, Nimisha Chaturvedi, Matthew Lueder |
| Maintainer: | Jelle Goeman <j.j.goeman at lumc.nl> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | penalized citation info |
| Materials: | |
| In views: | MachineLearning, Survival |
| CRAN checks: | penalized results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: | DIFtree, structree |
|---|---|
| Reverse imports: | c060, DIFboost, DIFlasso, GSelection, hdnom, mispr, mvdalab, penalizedclr, pensim, scRecover, splmm |
| Reverse suggests: | catdata, confSAM, flowml, fscaret, globaltest, lda, mlr, ordinalNet, peperr, riskRegression, tramnet |
Linking:
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