Hi!

I am wondering how I can fit correlations between random effects in non-linear models. For example:

```
fit_loss <- brm(
bf(cum ~ ult * (1 - exp(-(dev/theta)^omega)),
ult ~ 1 + (1|AY), omega ~ 1, theta ~ 1 + (1|AY),
nl = TRUE),
data = loss, family = gaussian(),
prior = c(
prior(normal(5000, 1000), nlpar = "ult"),
prior(normal(1, 2), nlpar = "omega"),
prior(normal(45, 10), nlpar = "theta")
),
control = list(adapt_delta = 0.9)
```

So `ult`

and `theta`

now have a random effect on the interceptâ€¦but how can I fit their correlations?

Thanks.

Sebastian