Please share your Stan program and accompanying data if possible.

When including Stan code in your post it really helps if you make it as readable as possible by using Stan code chunks (```stan) with clear spacing and indentation. For example, use

```
data {
// Gaussian model
int W; // Number of weeks (typically 12)
int N; // Number of subjects
int Xdim; // Dimension of X - latent low dimensional structure of the phenotype
// Exogenous variables
int exo_q_num; // number of exogenous survey questions
real U[N,W,exo_q_num]; // exogenous survey questions - missing weeks were linearly interpolated outside of Stan
// Intertemporal choice
int<lower=0> idx_itc_obs[N,W]; // Indices of weeks WITH data
int<lower=1> P_itc; // number of parameters
int<lower=0> T_max_itc; // Max number of trials across subjects across weeks
int<lower=0> Tr_itc[N, W]; // Number of trials for each subj for each week
real<lower=0> delay_later[N, W, T_max_itc]; // Delay later - subj x weeks x trials
real<lower=0> amount_later[N, W, T_max_itc]; // Amount later - subj x weeks x trials
real<lower=0> amount_sooner[N, W, T_max_itc]; // Amount sooner - subj x weeks x trials
int<lower=-1, upper=1> choice_itc[N, W, T_max_itc]; // choice itc - 0 for instant reward, 1 for delayed reward - subj x weeks x trials
// reward over the tasks
real reward[N, W];
}
parameters {
real itc_k_pr[N,W]; // itc
real itc_beta_pr[N,W]; // itc
matrix[2,Xdim] C;
matrix[N, Xdim] X1;
real<lower=0, upper=1> eta[N]; //eta
}
transformed parameters {
real<lower=0> itc_k[N,W]; // change for the sake of consistency - k_itc
real<lower=0> itc_beta[N,W];
itc_k = exp(itc_k_pr); // itc
itc_beta = exp(itc_beta_pr); // itc
}
model {
to_vector(C) ~ normal(0, 0.05);
to_vector(X1) ~ normal(0, 0.1);
eta ~ normal(0.5, 0.1);// prior for eta
real X[N,W,Xdim];
real itc_grad_sum[N, W, 2];
for (s in 1:2) {
real reward_sum = 0;
for (w in 1:5) {
reward_sum += reward[s, w];
matrix[2, Tr_itc[s,w]] itc_grad ;
if (w == 1) {
X[s,w,] = to_array_1d(inv_logit(X1[s,]));
} else {
vector[Xdim] a = eta[s].*(reward[s, w] - reward_sum/w)* C' * to_vector(itc_grad_sum[s, w-1]);
vector[Xdim] b = to_vector(X[s,w-1,]);
X[s,w,] = to_array_1d( b + a); // 68
}
itc_k_pr[s,w] ~ normal(C[1,] * to_vector(X[s,w,]),1);
itc_beta_pr[s,w] ~ normal(C[2,] * to_vector(X[s,w,]),1);
vector[Tr_itc[s,w]] ev_later = to_vector(amount_later[s,w,:Tr_itc[s,w]]) ./ (1 + itc_k[s,w] * to_vector(delay_later[s,w,:Tr_itc[s,w]])/7);
vector[Tr_itc[s,w]] ev_sooner = to_vector(amount_sooner[s,w,:Tr_itc[s,w]]);
choice_itc[s,w,:Tr_itc[s,w]] ~ bernoulli_logit(itc_beta[s,w] * (ev_later - ev_sooner));
vector[Tr_itc[s,w]] itc_p = 1 ./ (1 + exp(- itc_beta[s,w] * (ev_later - ev_sooner)));
vector[Tr_itc[s,w]] log_likelihood_p_grad = to_vector(choice_itc[s,w,:Tr_itc[s,w]]) ./ itc_p - (1 - to_vector(choice_itc[s,w,:Tr_itc[s,w]])) ./ (1 - itc_p );
itc_grad[1,]= to_row_vector(log_likelihood_p_grad .* itc_p .* (1 - itc_p) * itc_beta[s,w] * exp(itc_k_pr[s,w]) .* (- ev_later .* to_vector(delay_later[s,w,:Tr_itc[s,w]])/7 ./ (1 + itc_k[s,w] * to_vector(delay_later[s,w,:Tr_itc[s,w]])/7))); // grad of k
itc_grad[2,] = to_row_vector(log_likelihood_p_grad .* itc_p .* (1 - itc_p)*exp(itc_beta_pr[s, w]) .* (ev_later - ev_sooner)); // grad of beta
vector[Tr_itc[s,w]] ones_rep = to_vector(rep_array(1, Tr_itc[s,w]));
itc_grad_sum[s, w, ] = to_array_1d(itc_grad * ones_rep);
}
}
}
```

I tried to fit this model but it got stuck at chain 1 - done: 100/210. Everytime it got stuck here. Is there any idea what would cause it and what I could do to further investigate it? Sorry I am new to Stan and do not quite know the underground theories about it.