Nothing

```
context("cgpd")
test_that("cgpd family behaves as it ought", {
skip_on_cran()
skip_on_travis()
# We have extensive tests for gpd, so we really just need to be confident that
# cgpd reproduces it quite closely and that the behaviour near -1/2 makes sense.
tol <- .001
gm <- evm(rain, th=30)
cgm <- evm(rain, th=30, family=cgpd)
expect_equal(coef(gm)[1], coef(cgm)[1], tolerance=tol, label="cgpd and gpd fit same scale parameters by MLE")
expect_equal(unname(coef(gm)[2]), unname(exp(coef(cgm)[2]) - 1/2), tolerance=tol,
label="cgpd and gpd fit same shape parameters by MLE")
expect_equal(cor(resid(gm), resid(cgm)), 1, label="cgpd and gpd produce equivalent residuals from rain data by MLE")
pgm <- predict(gm, ci.fit=TRUE, M=seq(200, 500, by=100))$obj
pcgm <- predict(cgm, ci.fit=TRUE, M=seq(200, 500, by=100))$obj
pgm.m <- unlist(lapply(pgm, function(x) x[, 1]))
pcgm.m <- unlist(lapply(pcgm, function(x) x[, 1]))
# CIs are based on quadratic approximations that will not be identical
# Also, exp(eta) - 1/2 is not exactly xi because of where the optimizer
# chooses to give up. So when we extrapolate return levels, they are not
# identical.
#expect_equal(pgm[[1]], pcgm[[1]], tolerance=tol) currently failing but still under development so commented out
#bgm <- evm(rain, th=30, method="sim")
#bcgm <- evm(rain, th=30, method="sim", family=cgpd)
#pbgm <- predict(bgm, ci.fit=TRUE, M=seq(500, 1000, by=100))$obj
#pbcgm <- predict(bcgm, ci.fit=TRUE, M=seq(500, 1000, by=100))$obj
# How to judge if results are within simulation error?
})
```

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