I want to fit some model in Stan. One of the important steps is to fit the correlation between 2 uniformly [0,1] distributed variables.

Dummy data generation:

```
N = 10 # number of trials
sigma = 0.1 # add some noise
x<-runif(N,min=0,max=1)
y <- runif(N,min=(x-sigma),max=(x+sigma))
x <-(x-min(x))/(max(x)-min(x))
y <- (y-min(y))/(max(y)-min(y))
```

Can I use the standard approach, the same as for the normal distribution (lkj or lkj_corr_cholesky priors and so on) or are there better approaches for dealing with such an issue?