Performs Cox and Oakes test for the composite hypothesis of exponentiality, see e.g. Henze and Meintanis (2005, Sec. 2.5).

1 | ```
co.exp.test(x, simulate.p.value=FALSE, nrepl=2000)
``` |

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

`simulate.p.value` |
a logical value indicating whether to compute p-values by Monte Carlo simulation. |

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

The Cox and Oakes test is a test for the composite hypothesis of exponentiality. The test statistic is

*
CO_n = n+∑_{j=1}^n(1-Y_j)\log Y_j,
*

where *Y_j=X_j/\overline{X}*. *(6/n)^{1/2}(CO_n/π)* is asymptotically standard normal (see, e.g., Henze and Meintanis (2005, Sec. 2.5)).

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

`statistic` |
the value of the Cox and Oakes statistic. |

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

`method` |
the character string "Test for exponentiality based on the statistic of Cox and Oakes". |

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

Alexey Novikov, Ruslan Pusev and Maxim Yakovlev

Henze, N. and Meintanis, S.G. (2005): Recent and classical tests for exponentiality: a partial review with comparisons. — Metrika, vol. 61, pp. 29–45.

1 2 | ```
co.exp.test(rexp(100))
co.exp.test(runif(100, min = 0, max = 1))
``` |

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