As several Extreme Value distributions are parameterized for high extreme values, reversed functions for minima (e.g. low flow statistics) are derived. Reversing is done by fitting to the negated data (`-x`

), subtracting probabilities from one (`1 - f`

) and computing the negated probabilities.

1 2 3 | ```
cdf_ev(distribution, x, para)
pel_ev(distribution, lmom, ...)
qua_ev(distribution, f, para)
``` |

`distribution` |
character vector of length one containing the name of the distribution. The family of the chosen distribution must be supported by the package lmom. See |

`x` |
Vector of quantiles. |

`f` |
Vector of probabilities. |

`para` |
Numeric vector containing the parameters of the distribution, in the order zeta, beta, delta (location, scale, shape). |

`lmom` |
Numeric vector containing the L-moments of the distribution or of a data sample. E.g. as returned by |

`...` |
parameters like |

cdf_ev gives the distribution function; qua_ev gives the quantile function.

1 2 3 4 5 6 7 8 9 10 | ```
data("ngaruroro")
ng <- as.xts(ngaruroro)
minima <- as.vector(apply.yearly(ng$discharge, min, na.rm = TRUE))
# Weibull distribution and reversed GEV give the same results
distr <- "wei"
qua_ev(distr, seq(0, 1, 0.1), para = pel_ev(distr, samlmu(minima)))
distr <- "gevR"
qua_ev(distr, seq(0, 1, 0.1), para = pel_ev(distr, samlmu(minima)))
``` |

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