Description Usage Arguments Value References Examples

View source: R/smoothedLasso.r

Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the elastic net.

1 | ```
elasticNet(X, y, alpha)
``` |

`X` |
The design matrix. |

`y` |
The response vector. |

`alpha` |
The regularization parameter of the elastic net. |

A list with six functions, precisely the objective *u*, penalty *v*, and dependence structure *w*, as well as their derivatives *du*, *dv*, and *dw*.

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J Roy Stat Soc B Met, 67(2):301-320.

Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., and Qian, J. (2020). glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. R-package version 4.0.

Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788.

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