I am writing a hierarchical BUGS model that involves both linear and angle variables. I want the hyper-parameters to be normally distributed, which is straight-forward for the linear variables, but I'm not sure what to do about the angle variables. I would gladly use a von Mises distribution or a truncated normal, but neither is available in the BUGS language. My angle variables range from $0$ to $\pi$. 

Here is my model:

```
model {

   # parameters
   for ( i in 1:P ) {
      Y[i] ~ dnorm(Yhat[i], eachtau[plot[i]])
      Yhat[i] <- C[plot[i]] + a[plot[i]] * exp(-v[plot[i]] * year[i]) * cos(w[plot[i]] * year[i] - phi[plot[i]])
   }

   # hyperparameters
   for ( i in 1:M ) {
      C[i] ~ dnorm(mu.C, tau.C)
      a[i] ~ dnorm(mu.z, tau.z)
      v[i] ~ dnorm(mu.v, tau.v)

      # what do we do with these angles?!
      w[i] ~ ?
      phi[i] ~ ?

      eachtau[i] ~ dgamma(0.001, 0.001)
   }

   # priors
   mu.C ~ dnorm(0.0, 0.001)
   tau.C ~ dgamma(0.001, 0.001)
   mu.z ~ dnorm(0.0, 0.001)
   tau.z ~ dgamma(0.001, 0.001)
   mu.v ~ dnorm(0.0, 0.001)
   tau.v ~ dgamma(0.001, 0.001)
}

```

If it's important, I'm actually using JAGS (rather than OpenBUGS or WinBUGS), using the rjags package in R, but I believe the model syntax is the same for both.

What's the simplest way to model the angle variables with something like a normal in BUGS?