What are good Stan coding approaches to the following problems?

- I would like to write Stan code for a regression model that may or may not have random effects to handle subject clustering (Gaussian random effects). If the current model does not have random effects I can set the number of clusters to 1 and the prior on the variance of random effects to have virtually all its mass at zero. Is there a much better way to handle such switching?
- In general I would like to fit models with and without a certain set of parameters (representing special fixed effects). Can I pass from
`rstan`

a zero length data element (subset of covariance matrix) that is associated with those parameters? And how do I tell Stan that a vector of parameters is to be considered known (a vector of zeros) and not sampled, which will effectively exclude these parameters from consideration?