How can I create a positive ordered variable where the largest element is constrained to be 1.0?

`positive_ordered`

does not allow an `<upper=X>`

specification unfortunately…

Suppose I have a positive ordered variable A with n elements, ie

```
parameters{
positive_ordered[k] A;
}
```

Now suppose that I require that the last element of A is equal to 1.0.

One way to do this would be as follows

```
parameters{
positive_ordered[k] A_prime;
}
transformed parameters{
vector[k] A;
A = A_prime/A_prime[k];
}
```

My worry is that this leaves A_prime unidentified - multiplication of all elements of A_prime by the same number leaves the likelihood unchanged.

Other possible options:

- Put a prior on A eg
`A ~ normal(1, 0.0001)`

for an approximate constraint, although this is likely to have crappy sampling - Define
`positive_ordered[k-1] A_prime`

then require have`A_prime[k-1] ~ uniform(0,1)`

, although this breaches the warning on p125 of the manual about having interval constraints that are lower than the support of the parameter. - Define
`positive_ordered[k-1] B`

and then calculate`A = tanh(B)`

and fill in`A[k]=1.0`

. The hyperbolic tangent function will map the range (0,infinity) to (0,1).

The last one seems like the most reasonable…

[@Bob_Carpenter edited this to get the formatting for the code right.]