Hi all, I have a response variable that is non-normal and has several peaks. The scale it between 0-1$, with intervals of 0.01. I tried to run a Gaussian model at first but the pp_check plots look quite bad. Based on a previous post here (Gaussian vs. skew-normal model selection), I also tried to run a beta-binomial model after converting my response variable to cents instead of dollars by multiplying it by 100.

Here is my model syntax for the two models

```
brm(ResponseCents | vint(100) ~block*f_transition* f_manner + (1 + block || id , family = beta_binomial2, data = aggrdata, chains = 4, cores = n_cores-1, iter = 4000, warmup = 2000, control = list(adapt_delta = 0.9999, max_treedepth = 15), stanvars = stanvars, inits = "0")
```

```
ResponseModel1 <- lme4::lmer(response ~ block*f_transition* f_manner + (1 + block || id ), data = aggrdata, control = lmerControl(optimizer = "Nelder_Mead", optCtrl=list(maxfun=200000)))
```

What other types of distributions might fit better?