// Based on lrmc.stan by Ben Goodrich
// This is for the unconstrained partial PO model, which does not handle the
// second set of contrasts for the non-PO part of the model
functions {
  // pointwise log-likelihood contributions
  vector pw_log_lik(vector alpha, vector beta, matrix tau, vector gamma,
	                  array[] row_vector X, array[] row_vector Z, array[] int y, array[] int cluster) {
    int N = size(X);
    vector[N] out;
    int k = max(y); // assumes all possible categories are observed
		int Nc = num_elements(cluster);
		int j;
		real cj;
		real cj1;
    for (n in 1:N) {
      real eta = X[n] * beta;
			if(Nc > 0) eta = eta + gamma[cluster[n]];
      j = y[n];
			if (j == 1)        cj  = -( alpha[1] + eta );
			else if (j == 2)   cj  = alpha[1] + eta;
			else               cj  = alpha[j - 1] + eta + Z[n] * tau[ , j - 2];
			if(j > 1 && j < k) cj1 = alpha[j] + eta + Z[n] * tau[ , j - 1];
      if (j == 1 || j == k) out[n] = log_inv_logit(cj);
			else out[n] = log(1./(1. + exp(-cj)) - 1./(1. + exp(-cj1)));
//      else out[n] = log_diff_exp(-log1p_exp(- cj),
//                                 -log1p_exp(- cj1));
//			else out[n] = log(-log1p_exp(-cj) + log1p_exp(-cj1));
    }
    return out;
  }
  // Pr(y == j)
  matrix Pr(vector alpha, vector beta, matrix tau, vector gamma,
	          array[] row_vector X, array[] row_vector Z, array[] int y, array[] int cluster) {
    int N = size(X);
    int k = max(y); // assumes all possible categories are observed
    matrix[N, k] out;
		for(n in 1:N) {
      real eta = X[n] * beta + gamma[cluster[n]];
			for(j in 1 : k) {
			  real cj;
		    real cj1;
			  if (j == 1)        cj  = -( alpha[1] + eta );
			  else if (j == 2)   cj  = alpha[1] + eta;
			  else               cj  = alpha[j - 1] + eta + Z[n] * tau[ , j - 2];
			  if(j > 1 && j < k) cj1 = alpha[j] + eta + Z[n] * tau[ , j - 1];
				if (j == 1 || j == k) out[n, j] = log_inv_logit(cj);
				else out[n, j] = log(1./(1. + exp(-cj)) - 1./(1. + exp(-cj1)));
//				else  out[n, j] = log_diff_exp(-log1p_exp(-cj),
//                                   -log1p_exp(-cj1));
//				else out[n, j] = log(-log1p_exp(-cj) + log1p_exp(-cj1));
			}
    }
    return exp(out);
  }
}
data {
  int<lower = 1> N;   // number of observations
  int<lower = 0> Nc;  // number of clusters  (0 = no clustering)
  int<lower = 1> p;   // number of predictors
	int<lower = 1> q;   // number of non-PO predictors in Z
	int<lower = 0> cn;  // number of contrasts given priors
  matrix[N, p] X;     // matrix of CENTERED predictors
	matrix[N, q] Z;     // matrix of CENTERED PPO predictors
	matrix[cn, p] C;    // contrasts
  int<lower = 2> k;   // number of outcome categories
  // int<lower = 1, upper = k> y[N]; // outcome on 1 ... k
  // int<lower = 1, upper = Nc> cluster[Nc == 0 ? 0 : N];  // cluster IDs
  array[N] int<lower = 1, upper = k> y; // outcome on 1 ... k
  array[Nc == 0 ? 0 : N] int<lower = 1, upper = Nc> cluster;  // cluster IDs
  // prior standard deviations
	vector<lower = 0>[q] sdsppo;
	// prior means and SDs for contrasts
	vector[cn] cmus;
	vector[cn] csds;
  int<lower = 1, upper = 2> psigma;  // 1=t(4, rsdmean, rsdsd); 2=exponential
  // real<lower = 0> rsdmean[Nc == 0 ? 0 : 1];  // mean of prior for sigma
  // real<lower = 0> rsdsd[Nc == 0 || psigma == 2 ? 0 : 1];
  array[Nc == 0 ? 0 : 1] real<lower = 0> rsdmean;  // mean of prior for sigma
  array[Nc == 0 || psigma == 2 ? 0 : 1] real<lower = 0> rsdsd;
	// scale parameter for sigma (used only if psigma=1)
  real<lower = 0> conc;
}
transformed data {
  array[N] row_vector[p] Xr; //row_vector[p] Xr[N];
  array[N] row_vector[q] Zr; //row_vector[q] Zr[N];
  for (n in 1:N) Xr[n] = X[n, ];
  for (n in 1:N) Zr[n] = Z[n, ];
}
parameters {
  vector[p] beta; // coefficients on X
  matrix[q, k - 2] tau;  // coefficients on Z
  simplex[k] pi;  // category probabilities for a person w/ average predictors
  vector[Nc] gamma_raw;  // unscaled random effects
  // real<lower = 0> sigmag[Nc == 0 ? 0 : 1];   // SD of random effects
  array[Nc ==0 ? 0 : 1] real<lower = 0> sigmag;   // SD of random effects
}
transformed parameters {
  vector[k - 1] alpha;                               // intercepts
  vector[Nc] gamma = Nc == 0 ? gamma_raw : sigmag[1] * gamma_raw;             // scaled random effects
  vector[N] log_lik;                                 // log-likelihood pieces
  for (j in 2:k) alpha[j - 1] = logit(sum(pi[j:k])); // predictors are CENTERED
  log_lik = pw_log_lik(alpha, beta, tau, gamma, Xr, Zr, y, cluster);
}
model {
  if(Nc > 0) {
    gamma_raw ~ std_normal(); // implies: gamma ~ normal(0, sigmag)
    if(psigma == 1) sigmag ~ student_t(4, rsdmean[1], rsdsd[1]);
		else sigmag ~ exponential(1. / rsdmean[1]);
		}
  target += log_lik;
  target += dirichlet_lpdf(pi | rep_vector(conc, k));
	for (j in 1:(k - 2)) target += normal_lpdf(tau[ , j] | 0, sdsppo);
	if(cn > 0) target += normal_lpdf(C * beta | cmus, csds);
}