set.seed(0)
x = rnorm(1000)

# Assume bulk model parameterisation by default
fit = fnormgpd(x)

xx = seq(-5, 5, 0.01)
hist(x, breaks = 100, freq = FALSE)
lines(xx, dnormgpd(xx, fit$nmean, fit$nsd, fit$u, fit$sigma, fit$xi),
  col="blue",lwd=2)
abline(v = fit$u, col="blue")

# Change to parameterised approach
fit2 = fnormgpd(x, phiu=FALSE)

lines(xx, dnormgpd(xx, fit2$nmean, fit2$nsd, fit2$u, fit2$sigma, fit2$xi, fit2$phiu),
  col="red",lwd=2)
abline(v = fit2$u, col="red")

# Add continuity constraint
fit3 = fnormgpdcon(x)

lines(xx, dnormgpdcon(xx, fit3$nmean, fit3$nsd, fit3$u, fit3$xi, fit3$phiu),
  col="green",lwd=2)
abline(v = fit3$u, col="green")

help(evmix)

# Nonparametric bulk fit
fit4 = fkdengpd(x)

lines(xx, dkdengpd(xx, x, fit4$lambda, fit4$u, fit4$sigmau, fit4$xi, fit4$phiu),
  col="purple",lwd=2)
abline(v = fit4$u, col="purple")

# Hybrid Pareto
fit5 = fhpd(x)

lines(xx, dhpd(xx, fit5$nmean, fit5$nsd, fit5$xi), col="orange",lwd=2)
abline(v = fit5$u, col="orange")

# Usual model diagnostics default to focus on upper tail
evmix.diag(fit)

# Can see entire fit
evmix.diag(fit,upperfocus=FALSE)

# Usual MRL and threshold stability plots, with some extra features
par(mfrow=c(1,1))
mrlplot(x, try.thresh=c(0.5,1,1.5))

data(FtCoPrec,package="extRemes")
mrlplot(FtCoPrec[,5], try.thresh=c(0.395, 0.8, 1.2))