I have the following nonlinear setup

```
stan_inv_robit <- "
real inv_robit(real y) {
return(student_t_cdf(y, 1, 0, 1));
}
"
stanvar_inv_robit <- stanvar(scode = stan_inv_robit, block = "functions")
robit_formula <-
bf(dead | trials(n) ~ inv_robit(eta),
nlf(eta ~ b0 + b1*log10dose + b2*manual + b3*log10dose*manual),
b0 ~ 1 + (1|experiment),
b1 ~ 1 + (1|experiment),
b2+b3 ~ 1,
nl = TRUE)
```

and the non-robust model I am comparing to I specified as:

```
m1<-brm(dead | trials(n) ~ log10(dose)*platform + (1+log10(dose)|experiment),
data=dff, family=binomial(link=probit), save_pars = save_pars(all=TRUE),
cores = getOption("mc.cores",4), iter=10000, warmup=2000,
control = list(adapt_delta = 0.95, max_treedepth=15))
```

The non-robust version also estimates a correlation between the random intercept and random slope. How can I achieve this in the robust model as well?