Dear developers,

currently, if you model your data and you have some missed trials, you remove them from the data before modelling which is projected in the prep data by `Tsubj`

being less than `T`

. Then in the predictions STAN gives, it adds `T-Tsubj`

rows of value -1 for each subject. I would like to ask if there is a plan to add those -1 values to where they actually belong, where they are missing, and not to the end.

So for example, if I have trials 1,2,5 it will not give me y1,y2,y3,-1-1 but rather y1,y2,-1,-1,y5.

I guess it is quite a complex task but it would be very helpful.

Thanks.

I think the easiest way to handle this sort of processing will be using array indexing, probably outside of Stan.

In R something like:

```
y = c(1, 2, 3, 4, 5)
trials = c(1, 2, 5)
y_sub = y[trials]
```

And then do your stan model, and then:

```
y_rep = matrix(-1, nrow = N, 5)
# Assuming y_rep_subj is an Nx3 matrix of N draws of 3 predictions
y_rep[, trials] = y_rep_subj
```

Mess around with the code here to get the hang of what is happening:

```
y = matrix(-1, nrow = 10, ncol = 5)
y[, c(1, 2, 5)] = matrix(3, nrow = 10, ncol = 3)
```

Yes, that what I was doing outside with Python as well, gets a bit tricky with more subjects/dimensions of the data. Thatâ€™s why I was asking if there is any incentive to do that inside STAN.

Thanks for the reply.