Fit multiple models with L1 group penalty. QICD algorithm is using an adaptation of the algorithm presented by Peng and Wang (2015).

1 2 | ```
groupMultLambda(x, y, groups, tau = 0.5, lambda, intercept = TRUE,
penalty="LASSO", alg="QICD", ...)
``` |

`x` |
Matrix of predictors. |

`y` |
Vector of response values. |

`groups` |
Vector assigning columns of x to groups. |

`tau` |
Conditional quantile being modelled. |

`lambda` |
Vector of lambdas. Default is for lambdas to be automatically generated. |

`intercept` |
Whether model should include an intercept. Constant does not need to be included in "x". |

`penalty` |
Type of penalty: "LASSO", "SCAD" or "MCP". |

`alg` |
"QICD" for QICD implementation. Otherwise linear programming approach is implemented. |

`...` |
Additional parameters to be sent to rq.group.fit. |

Returns a list of rq.group.pen objects. Each element of the list is a fit for a different value of lambda.

Ben Sherwood

[1] Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with
grouped variables. *J. R. Statist. Soc. B*, **68**, 49-67.

[2] Peng, B. and Wang, L. (2015). An Iterative Coordinate Descent Algorithm for
High-Dimensional Nonconvex Penalized Quantile Regression.
*Journal of Computational and Graphical Statistics*, **24**, 676-694.

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