Constructing prior distibutions for the location, scale and shape parameters using normal, beta or gamma distributions. A linear trend for the location can also be specified, using a prior normal distribution centered at zero for the trend parameter.

1 2 3 4 | ```
prior.prob(quant, alpha, trendsd = 0)
prior.quant(prob = 10^-(1:3), shape, scale, trendsd = 0)
prior.norm(mean, cov, trendsd = 0)
prior.loglognorm(mean, cov, trendsd = 0)
``` |

`quant, alpha` |
Numeric vectors of length three and four
respectively.
Beta prior distibutions are placed on probability ratios
corresponding to the quantiles given in |

`prob, shape, scale` |
Numeric vectors of length three.
Gamma prior distibutions, with parameters |

`mean, cov` |
The prior distibution for the location, log(scale)
and shape is taken to be trivariate normal, with mean |

`trendsd` |
The standard deviation for the marginal normal prior distribution (with mean zero) placed on the linear trend parameter for the location. If this is zero (the default) a linear trend is not implemented. |

See the user's guide.

Returns an object of class `"evprior"`

, which is essentially
just a list of the arguments passed.

1 2 3 4 | ```
mat <- diag(c(10000, 10000, 100))
prior.norm(mean = c(0,0,0), cov = mat, trendsd = 10)
prior.quant(shape = c(38.9,7.1,47), scale = c(1.5,6.3,2.6))
prior.prob(quant = c(85,88,95), alpha = c(4,2.5,2.25,0.25))
``` |

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