Hi Stan community! I’m an intermediate Stan user and first time forum poster. 99% of my Stan experience has been with PyStan, but for a current project I need to use RStan. I have Stan code I’ve developed locally and run in PyStan that works like a charm, but when I try to use the same Stan code in RStan it gives errors I’ve never seen before.

```
y ~ normal(beta0 + beta1 * exp(-D.^2 ./ r) * rep_vector(1, m), sigma);
```

The first error I get is:

```
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
Variable “D.” does not exist.
error in ‘model103d64bcfce11_thresholded_kernel’ at line 28, column 36
-------------------------------------------------
26:
27: // use vectorized operations to compute the kernel values,
28: y ~ normal(beta0 + beta1 * exp(-D.^2 ./ r^2) * rep_vector(1, m), sigma);
^
29: }
-------------------------------------------------
Error in stanc(file = file, model_code = model_code, model_name = model_name, :
failed to parse Stan model ‘thresholded-kernel’ due to the above error.
```

I was initially confused because the dot should be being parsed as part of the `.^`

operator. Wondering if there was an easy fix, I changed the code to the following:

```
y ~ normal(beta0 + beta1 * exp(-D .^ 2 ./ r) * rep_vector(1, m), sigma);
```

But that line gave a new error:

```
SYNTAX ERROR, MESSAGE(S) FROM PARSER:
error in ‘model103d65c8aff37_thresholded_kernel’ at line 28, column 32
-------------------------------------------------
26:
27: // use vectorized operations to compute the kernel values,
28: y ~ normal(beta0 + beta1 * exp(-D .^ 2 ./ r^2) * rep_vector(1, m), sigma);
^
29: }
-------------------------------------------------
PARSER EXPECTED: “(”
Error in stanc(file = file, model_code = model_code, model_name = model_name, :
failed to parse Stan model ‘thresholded-kernel’ due to the above error.
```

Does anyone know why this code in particular isn’t working in RStan? Again, the model compiles and runs with no issues in the latest version of PyStan.

I’m on R version 4.3.1 and RStan version 2.21.8, in case those are relevant.