dir.stan

data {
  int<lower=1> n;
  int<lower=0> X[n, n];
  // [[Additional data go here]]
}
parameters {
  // [[Parameters go here]]
  // 
  // For example:
  // real<lower=0,upper=1> theta;
}
model {
  // [[Priors go here]]
  // 
  // For example:
  // theta ~ beta(1, 1/2);

  for (i in 1:n) {
    for (j in 1:n) {
      if (i != j) {
        // [[Data model goes here]]
        real log_mu_ij_0 = ;
        real log_mu_ij_1 = ;

        // [[Network model goes here]]
        real log_nu_ij_0 = ;
        real log_nu_ij_1 = ;

        // Boilerplate code below, do not modify
        real z_ij_0 = log_mu_ij_0 + log_nu_ij_0;
        real z_ij_1 = log_mu_ij_1 + log_nu_ij_1;
        if (z_ij_0 > z_ij_1) {target += z_ij_0 + log1p_exp(z_ij_1 - z_ij_0);}
        else {target += z_ij_1 + log1p_exp(z_ij_0 - z_ij_1);}
      }
    }
  }
}
generated quantities {
  // Generate edge probability matrix 
  real Q[n ,n];
  for (i in 1:n) {
    Q[i, i] = 0;
    for (j in 1:n) {
      if (i != j) { 
        // [[Data model goes here, as in model block]]
        real log_mu_ij_0 = ;
        real log_mu_ij_1 = ;

        // [[Network model goes here, as in model block]]
        real log_nu_ij_0 = ;
        real log_nu_ij_1 = ;

        // Boilerplate code below, do not modify
        real z_ij_0 = log_mu_ij_0 + log_nu_ij_0;
        real z_ij_1 = log_mu_ij_1 + log_nu_ij_1;
        Q[i, j] = 1 / (1  + exp(z_ij_0 - z_ij_1));
      }
    }
  }
}