Performs model averaging on a set of nested candidate models with the weights vector chosen such that a specific Mallow's criterion is minimized.

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`X` |
A dataframe or matrix of data. |

`formula` |
Formula of the full model. |

`ycol` |
Either a character vector or integer specifying the column with the outcome variable. |

`variance` |
A character vector specifying whether the variance is estimated due to the formula of Burnham
and Anderson ( |

`bsa` |
A positive integer specifying the number of bootstrap samples used if |

Mallow's Model Averaging (MMA) considers all nested submodels of the full model as candidate models, i.e if there are 7 variables there are 7 candidate models.
The weight vector used to combine the respective estimates is chosen such that a certain Mallow's type criterion is minimized. The final weighted estimate
targets to minimize the mean squared prediciton error and is optimal in some sense, see Theorem 1 and Lemma 3 in Hansen, B. (2007, *Least Squares Model Averaging*, Econometrica, 75:1175-1189).

Note however that the results of MMA depend on the ordering of the regresssors.

Returns an object of `class`

‘mma’:

`coefficients` |
A matrix of estimated coefficients and standard errors (and bootstrap standard errors if |

`averaging.weights` |
A matrix containing the weights for each models used in the model averaging procedure. |

Michael Schomaker

Hansen, B. (2007), *Least Squares Model Averaging*, Econometrica, 75:1175-1189

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