Please also provide the following information in addition to your question:

- Operating System: Mac OS High Sierra
- brms Version: 2.3.1

I recently read Adrian Baez-Ortega’s great blog on “Bayesian robust correlation with Stan in R” (https://baezortega.github.io/2018/05/28/robust-correlation/), in which he used the Student’s t distribution to handle outliers in correlations. If you use the default `family = gaussian`

, specify multiple response variables (e.g., with the `cbind()`

syntax), and fit an intercept-only model, you can get Bayesian correlations within brms. E.g., this works great:

```
N <- 1000
set.seed(1)
d <-
tibble(x = rnorm(N, 0, 1),
y = rnorm(N, mean = 0 + 1*x, 1))
fit1 <-
brm(data = d, family = gaussian,
cbind(x, y) ~ 1)
print(fit1)
```

However, the method fails when switching to `family = student`

. When I try the following:

```
fit2 <-
brm(data = d, family = student,
cbind(x, y) ~ 1,
cores = 4)
```

I get this error message:

“Error in new_CppObject_xp(fields$.module, fields$.pointer, …) :

Exception: modeldcb6526990f7_filedcb64a253b90_namespace::modeldcb6526990f7_filedcb64a253b90: nu_x is 0, but must be greater than or equal to 1 (in ‘modeldcb6526990f7_filedcb64a253b90’ at line 26)”

In the “lb” subsection in the “set_prior” section of the brms reference manual I’m told “Lower bound for parameter restriction. Currently only allowed for classes ‘b’, ‘ar’, ‘ma’, and ‘arr’." And indeed when I attempt `lb = 1`

for nu:

```
fit3 <-
brm(data = d, family = student,
cbind(x, y) ~ 1,
prior = c(set_prior("gamma(2, .1)", class = "nu", lb = 1)),
cores = 4)
```

I get the error message: “Error: Currently boundaries are only allowed for population-level and autocorrelation parameters.”

And yet it works great if I simply go univariate.

```
fit4 <-
brm(data = d, family = student,
y ~ 1 + x,
cores = 4)
print(fit4)
```

Am I missing something?