Constructing sandwich covariance matrix estimators by multiplying bread and meat matrices for regularized regression parameters.

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`x` |
a fitted model object. |

`breadreg.` |
either a breadReg matrix or a function for computing
this via |

`meatreg.` |
either a breadReg matrix or a function for computing
this via |

`which` |
which penalty parameters(s) to compute? |

`log` |
if TRUE, the corresponding element is with respect to log(theta) in negative binomial regression. Otherwise, for theta |

`...` |
arguments passed to the |

`sandwichReg`

is a function to compute an estimator for the covariance of the non-zero parameters. It takes a breadReg matrix (i.e., estimator of the expectation of the negative
derivative of the penalized estimating functions) and a meatReg matrix (i.e.,
estimator of the variance of the log-likelihood function) and multiplies
them to a sandwich with meat between two slices of bread. By default
`breadReg`

and `meatReg`

are called. Implemented only for `zipath`

object with `family="negbin"`

in the current version.

A matrix containing the sandwich covariance matrix estimate for the non-zero parameters.

Zhu Wang <zwang@connecticutchildrens.org>

Zhu Wang, Shuangge Ma and Ching-Yun Wang (2015) *Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany*, *Biometrical Journal*. 57(5):867-84.

`breadReg`

, `meatReg`

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