I am trying to fit a “non-linear” model. Note that it’s not really non-linear, but the goal is to fit a change-score model (a consulting request) with missing values.

Conceptually, what I want is: `outcome ~ alpha + (mi(time2) - mi(time1))*beta`

. That is - There are missings in time2 and time 1 that I want to model from other predictors; after filling those in, I want to compute a difference score and linearly predict the outcome from it.

The following does not work (because `mi()`

is not a function in Stan). (Variables changed to be generic).

```
cs.bf <- brmsformula(outcome ~ alpha + (mi(time2) - mi(time1))*beta,nl=TRUE,beta ~ 1, alpha ~ 1) +
brmsformula(time1|mi() ~ z1 + z2) +
brmsformula(time2|mi() ~ z1 + z2)
```

Is there a way to specify this model?