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real lp_single_prior(vector x, int dist, row_vector pars1,
row_vector pars2, row_vector pars3){
real out = 0.0;
if(dist == 1){
out += normal_lpdf(x | pars1, pars2);
} else if(dist == 2){
out += uniform_lpdf(x | pars1, pars2);
} else if(dist == 3){
out += student_t_lpdf(x | pars1, pars2, pars3);
} else if(dist == 4){
out += logistic_lpdf(x | pars1, pars2);
} else if(dist == 5){
out += gamma_lpdf(x | pars1, pars2);
} else if(dist == 6){
out += double_exponential_lpdf(x | pars1, pars2);
}
return out;
}
real lp_priors(vector beta, array[] int dist, matrix pars){
int idx;
real out = 0.0;
int nb = num_elements(beta);
if(nb == 0) return out;
idx = dist[1] == 0 ? 1 : 2;
// intercept prior, if intercept exists
if(dist[1] != 0){
out += lp_single_prior(beta[1:1], dist[1], pars[1,1:1],
pars[2,1:1], pars[3,1:1]);
}
// regression coefficients priors, if there are any
if(dist[2] != 0){
out += lp_single_prior(beta[idx:nb], dist[2], pars[1,idx:nb],
pars[2,idx:nb], pars[3,idx:nb]);
}
return out;
}
real lp_random_prior(int has_random, int n_group_vars, vector b,
array[] int n_random, vector sigma, int dist, matrix pars){
int idx = 1;
real out = 0;
int par_idx = cols(pars);
row_vector[n_group_vars] rep_par1 = rep_row_vector(pars[1,par_idx], n_group_vars);
row_vector[n_group_vars] rep_par2 = rep_row_vector(pars[2,par_idx], n_group_vars);
row_vector[n_group_vars] rep_par3 = rep_row_vector(pars[3,par_idx], n_group_vars);
if(has_random){
out += lp_single_prior(sigma, dist, rep_par1, rep_par2, rep_par3);
for (i in 1:n_group_vars){
out += normal_lpdf(b[idx:(n_random[i]+idx-1)] | 0, sigma[i]);
idx += n_random[i];
}
}
return out;
}
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