evmc: Simulate Markov Chains With Extreme Value Dependence Structures

Description

Simulation of first order Markov chains, such that each pair of consecutive values has the dependence structure of one of nine parametric bivariate extreme value distributions.

Usage

evmc(n, dep, asy = c(1,1), alpha, beta, model = "log",
    margins = "uniform")

Arguments

Number of observations.

dep

Dependence parameter for the logistic, asymmetric logistic, Husler-Reiss, negative logistic and asymmetric negative logistic models.

asy

A vector of length two, containing the two asymmetry parameters for the asymmetric logistic and asymmetric negative logistic models.

alpha, beta

Alpha and beta parameters for the bilogistic, negative bilogistic, Coles-Tawn and asymmetric mixed models.

model

The specified model; a character string. Must be either "log" (the default), "alog", "hr", "neglog", "aneglog", "bilog", "negbilog", "ct" o

margins

The marginal distribution of each value; a character string. Must be either "uniform" (the default), "rweibull", "frechet" or "gumbel" (or any unique partial match), for the uniform, stan

Value

A numeric vector of length n.

synopsis

evmc(n, dep, asy = c(1,1), alpha, beta, model = c("log", "alog", "hr", "neglog", "aneglog", "bilog", "negbilog", "ct", "amix"), margins = c("uniform","rweibull","frechet","gumbel"))

Examples

Run this code

evmc(100, alpha = 0.1, beta = 0.1, model = "bilog")
evmc(100, dep = 10, model = "hr", margins = "gum")