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Simulate data from the multivariate power exponential distribution given the location, scale matrix, shape, and skewness parameter.
rspe(n, location = rep(0, nrow(scale)), scale = diag(length(location)), beta = 1, psi = c(0, 0))
Number of observations to simulate.
A \(p\)-dimensional vector. \(\mu\).
A \(p\)-dimensional square scale matrix \(\Sigma\).
A positive shape parameter \(\beta\) that determines the kurtosis of the distribution.
A \(p\)-dimensional vector determining skewness. \(\mu\).
A matrix with rows representing the \(p\)-dimensional observations.
Based on a Metropolis-Hastings rule.
# NOT RUN { dat <- rspe(n = 1000, beta = 0.75, location = c(0,0), scale = matrix(c(1,0.7,0.7,1),2,2), psi = c(5,5)) # }
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