Description Usage Arguments Details Value Author(s) References Examples

Performs Quesenberry–Miller test for the hypothesis of uniformity, see Quesenberry and Miller (1977).

1 | ```
quesenberry.unif.test(x, nrepl=2000)
``` |

`x` |
a numeric vector of data values. |

`nrepl` |
the number of replications in Monte Carlo simulation. |

The Quesenberry–Miller test for uniformity is based on the following statistic:

*
B_n = ∑_{i=1}^{n+1}{≤ft( X_{(i)} - X_{(i-1)} \right)^2} + ∑_{i=1}^{n}{≤ft( X_{(i)} - X_{(i-1)} \right)≤ft( X_{(i+1)} - X_{(i)} \right)},
*

where *X_{(0)}=0*, *X_{(n+1)}=1*.
The p-value is computed by Monte Carlo simulation.

A list with class "htest" containing the following components:

`statistic` |
the value of the Quesenberry–Miller statistic. |

`p.value ` |
the p-value for the test. |

`method` |
the character string "Quesenberry–Miller test for uniformity". |

`data.name` |
a character string giving the name(s) of the data. |

Maxim Melnik and Ruslan Pusev

Quesenberry, C.P. and Miller F.L. (1977): Power studies of some tests for uniformity. — J. Stat. Comput. Simul., vol. 5, pp. 169–191.

1 2 | ```
quesenberry.unif.test(runif(100,0,1))
quesenberry.unif.test(runif(100,0,1.05))
``` |

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