This function will generate the matrix of deltas, as specified in the paper. See Details section.

1 | ```
genDelMat(theta.diff, sigma.2 = 1, n = 1)
``` |

`theta.diff` |
A vector of length p-1, where p is the number of populations
of treatments. Coordinate [i] in theta.diff corresponds to |

`sigma.2` |
The known variance of the error terms. |

`n` |
The number of replications in each population. |

As specified in the paper, we can assume that the thetas are in a
decreasing order, meaning that *θ_1 ≥ θ_2, …, θ_n*.
It follows that all the components of the theta.diff vector must be positive.
Note that the delta matrix in the paper is a scaled version of the
differences between the thetas.

The function returns a matrix with p rows and p columns, that
contains the *delta_{ij}*'s, as described in the paper.

`exactCoverageProb`

, `integrand`

1 2 |

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