well, ordered transformation should also fit with our initial usecase - mapping constrained parameter (e.g., sorted vector) to unconstrained parameter. I think what happen here is kind of a scope creep of the kwarg, which we should ideally have constrain that apply to RVs, and transformation that apply to distributions.
For example, in Stan this is more transparent that constrain is for RVs, and unrelated to the underlying distribution:
parameters {
real<lower=0> sigma;
ordered[K-1] c;
}
model {
sigma ~ Normal # <== actually a halfnormal
}
}