git.stan

  
data {
  int<lower=0> n_trials;
  int<lower=0> n_dogs;
  int<lower=0,upper=1> y[n_dogs,n_trials];
}
parameters {
  vector[2] beta;
}
transformed parameters {
  matrix[n_dogs,n_trials] n_avoid;
  matrix[n_dogs,n_trials] n_shock;
  matrix[n_dogs,n_trials] p;
  
  for (j in 1:n_dogs) {
    n_avoid[j,1] = 0;
    n_shock[j,1] = 0;
    for (t in 2:n_trials) {
      n_avoid[j,t] = n_avoid[j,t-1] + 1 - y[j,t-1];
      n_shock[j,t] = n_shock[j,t-1] + y[j,t-1];
    }
    for (t in 1:n_trials)
      p[j,t] = inv_logit(beta[1] * n_avoid[j,t] + beta[2] * n_shock[j,t]);
  }
}
model {
  beta[1] ~ uniform(-100, 0);
  beta[2] ~ uniform(0, 100);
  for (i in 1:n_dogs) {
    for (j in 1:n_trials)
      y[i,j] ~ bernoulli(p[i,j]);
  }
}