I am trying to use non-linear syntax to estimate several parameters in a model with `brms`

where I am treating the outcome as a Beta proportion and trying to infer several other parameters.

The general specification is:

S_{r,t}, C, and R are observed, so I want to estimate I, B, a, b, and c

I first tried

```
bform <- bf(
Srt ~ I,
I ~ 1 + (C * B )+ (1|r) + (1|t),
B ~ 1 + (a * R^b + c),
a ~ 1,
b ~ 1,
c ~ 1,
nl = TRUE
)
```

But, that gives me a power error:

`Error in terms.formula(as.formula(x)) : invalid power in formula`

I assumed this was do to how R is interpreting the `^`

So I eliminated b and assumed a `2`

```
bform <- bf(
seroprev ~ I,
I ~ 1 + (C * B + (1|r) + (1|t),
B ~ 1 + (a * R^2 + c),
a ~ 1,
c ~ 1,
nl = TRUE
)
```

This gives a different error:

`Error: The parameter 'B' is not a valid distributional or non-linear parameter. Did you forget to set 'nl = TRUE'?`

I have set `nl = TRUE`

.

Is it possible to estimate multiple parameters this way? Or should I be â€śinjectingâ€ť these parameters into the `transformed parameters`

block somehow?