Priors for rsstap models

## GLMs

Using one spatial aggregated predictor as an example, stap_(g)lm models have the following form.

$$g(\mu_i) = Z_i^T \delta + \sum_d \sum_l \beta_l\phi_l(d)$$

Currently the priors in rsstap are fixed and are always of the following form:

$$p(\delta) \propto 1$$

$$\sigma \sim C^+(0,5)$$

$$\beta \sim MVN_L(0,\sum_k S_k \tau_k)$$

$$\tau_k \sim Exp(1)$$

Where $$S_k$$ are generated from the jagam function and sum to form a complete precision matrix with different $$\tau$$ penalties along the diagonal.

## GLMERs

Using only one spatial aggregated predictor as an example, stap_(g)lmer models have the following form:

$$g(\mu_{ij}) = Z_{ij}^T \delta + \sum_d \sum_l \beta_l\phi_l(d) + W_{ij}^Tb_i$$

Where

$$b_i \sim N(0,\Sigma)$$

priors for $$\delta$$,$$\beta$$,$$\sigma$$,$$\tau_k$$ are the same as before, but now $$\Sigma$$ is decomposed as described here.