Hi,

What is the best way in brms() to add group-level predictors to explain group-level parameters? In my case the group-level parameter is the intercept which varies across year groups, say.

I am following the Gelman and Hill route and simply adding in a group-level predictor outside of the group-level code block. For example, adding in a predictor to explain the intercept which varies between year groups, by creating a group-predictor - ** meanCF** - for the group-varying intercept. This predictor - by definition - only varies between year to year in my dataset:

`y ~ X + meanCF + (1 | year)`

```
reg_data <- data %>%
group_by(year) %>%
mutate(medianCF = median(cashflow_rf),
meanCF = sum(cashflow)/sum(fixed_c_gross))
```

Is this the best approach?

Many thanks,