Hello, I am setting up a multivariate model in brms. The data consists of subjects who received treatment at 4 different time points. Due to the nature of the data, the resp_subset function was used to subset out the different time periods resulting in 4 response variables (**Group1_response_value**, **Group2_response_value**, etc), two predictor variables (our time variable, **TimeVar** and another, **var2**) and a group level effect for the subject intercept (1|p|study_participant).

The generic model code used was:

```
bform_1 <-
bf(Group1_response_value | resp_subset(sub1) ~ TimeVar_group1 + var2 + (1|p|study participant)) +
bf(Group2_response_value | resp_subset(sub2) ~ TimeVar_group2 + var2 + (1|p|study participant)) +
bf(Group3_response_value | resp_subset(sub3) ~ TimeVar_group3 + var2 + (1|p|study participant)) +
bf(Group4_response_value | resp_subset(sub4) ~ TimeVar_group4 + var2 + (1|p|study participant)))
Model <- brm(bform_1, df)
```

This code produced an expected outcome but -due to the nature of our data, we would like to model the parameters on each **TimeVar** (between each univariate model, so **TimeVar_group1**, **TimeVar_group2**, etc) as correlated. Iâ€™ve read through the vignettes and the updated documentation from Nov 2020 and canâ€™t find a function that will do this. Iâ€™ve seen in the brms formula vignette that we can indicate correlation between group level terms by â€ś*using || instead of |. All group-level terms sharing the same ID will be modeled as correlated*â€ť. Is there a way to do something similar to this for our population level regression parameter- and also indicate it would be for just one of the predictor terms?

Operating System: Windows 10, 64-bit

R version 4.0.3, brms version 2.14.4

Thanks!