Description Usage Arguments Value Author(s) References Examples

The null-distribution of the test statistics under
inequality constraints takes the form of mixtures of F-distributions.
This function computes these mixing weights (a.k.a chi-bar-square weights
and level probabilities). It can be used directly and is called by
the `conTest`

function.

1 2 3 |

`VCOV` |
variance-covariance matrix of the data for which the weights are to be calculated. |

`Amat` |
constraints matrix |

`meq` |
integer (default = 0) treating the number of
constraints rows as equality constraints instead of inequality
constraints. For example, if |

`R` |
integer; number of bootstrap draws for |

`parallel` |
the type of parallel operation to be used (if any). If missing, the default is set "no". |

`ncpus` |
integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs. |

`cl` |
an optional parallel or snow cluster for use if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the conTest call. |

`seed` |
seed value. |

`verbose` |
logical; if TRUE, information is shown at each bootstrap draw. |

`...` |
no additional arguments for now. |

The function returns a vector with the mixing weights

Leonard Vanbrabant and Yves Rosseel

Silvapulle, M.J. and Sen, P.K. (2005, p.79). *Constrained
Statistical Inference*. Wiley, New York.

1 2 3 4 5 6 7 | ```
W <- matrix(c(1,0.5,0.5,1),2,2)
Amat <- rbind(c(0,1))
meq <- 0L
# we only generate 99 bootstrap samples in this
# example; in practice you may wish to use a much higher number.
wt.bar <- con_weights_boot(W, Amat, meq, R = 99)
wt.bar
``` |

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