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 Lasso.

1 | ```
standardLasso(X, y, lambda)
``` |

`X` |
The design matrix. |

`y` |
The response vector. |

`lambda` |
The Lasso regularization parameter. |

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

Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. J Roy Stat Soc B Met, 58(1):267-288.

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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