Although there is not (yet) a way to run different parts of the signal flow diagram at different sample rates, there is a way to emulate oversampling (which it sounds like Alan & Pete may be doing?):

Imagine you have a stateless function f(x). To emulate n-times oversampling, you could make n copies of f(x) and let each one operate on a different time step, where there are now n time steps for each sample at the old SR.

Since the rest of the signal flow graph is feeding f(x) at SR, where do those n new samples come from (the time steps "in between" the actual input samples?)

To guess what the values are between the actual input samples, we can interpolate from one sample to the next. One simple way to do this would be to use linear interpolation. If your input samples are A and B, the n samples in between would be (A * (n - (i - 1)) + B * (i - 1)) / n

So the sample points "in between" A and B would be:

x1 = A

x2 = (A * (n - 1)) + B) / n

...

xn = (A + B * (n - 1)) / n

Now we can apply the function to each of the new time points:

f(x1), f(x2), .., f(xn)

which you can think of as n-samples of a signal running at SR*n.

To use the output of this higher rate signal in combination with the rest of the signal flow graph, you have to filter it at half of the sample rate (i.e., put it through lowpass filter whose cutoff is SR/2). For example, one simple (but not very high quality) way to low-pass filter the signal would be by averaging:

[f(x+1) + f(x+2) + .. + f(x + n)] / n

In other words, you could take the sum of the outputs of the n copies of f(x) and scale by 1/n and use this output with the rest of your signal flow graph.

The emulation could be improved by using a more complicated interpolation function on the input and a better low-pass filter on the SR*n signal output.