@paul.buerkner suggested `posterior_smooths`

as a substitute/work-around for `hypothesis()`

for models with splines (Using **hypothesis()** with **s()** from **brms**).

However, I can‘t get to grips with this approach.

I try to explain what I want to achieve:

```
seed <- 0
dat <- mgcv::gamSim(1, n = 200, scale = 2)
fit <- brm(y ~ s(x0), data = dat)
conditional_effects(fit)
```

From this plot, how do I calculate the posterior probability for e.g. that y increases by more than 2 between x0=0 and x0=0.5?

Thanks!