Description Usage Arguments Details Value

`penalty_weights`

calculates the weights associated with the
observations in the augmented dataset used in fitting the penalized partially
linear quantile regression.

1 2 | ```
penalty_weights(beta_vec, n, penalty_type = NULL, penalty_deriv = NULL,
lambda, a = NULL, ...)
``` |

`beta_vec` |
a numeric vector containing the current estimates of the regression parameters from the linear portion of the model. |

`n` |
the number of observations in the data set. |

`penalty_type` |
a character string indicating which penalty function should be used. See Details for more information. |

`penalty_deriv` |
a user-specified function for use in creating the weights. See Details for more information. |

`lambda` |
a scalar corresponding to the tuning parameter. |

`a` |
a scalar for use in the SCAD and MCP penalty functions. |

`...` |
(optional) additional arguments to be passed to the user-supplied penalty derivative function |

The weights are based on the derivative of the penalty function used in defining the penalized regression problem. There are two ways for specifying the derivative of the penalty function. First, the user can provide the name of the penalty function through the argument penalty_type. Based off this specification, the derivative is then computed using built-in functions. The current options for the penalty function are "SCAD" and "MCP". Alternatively, the user can provide their own penalty derivative function. This function should take as an argument a numeric vector and return a numeric vector of the same length.

a vector of length (n + 2*length(beta_vec)) containing the weights associated with the observations in the augmented data set.

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