> For my own modules, I've tended to follow the example of the Distributions
> and Distances package, where each distribution (or distance) is its own
> type, and then there is a small number of generic functions (eg "evaluate")
> that use multiple dispatch to return the appropriate value depending on the
> input type.
If I recall correctly, recent discussions on this list suggest that many
(but not all) see this as the most julian pattern. I think it may have
been discussed within one of those recent super-long threads.
> But I've seen source code in other packages that indicates a different
> strategy. For example, the Optim package appears (and please correct
> me if I'm wrong - I'm definitely not an expert) to have a different
> Symbol for each optimisation algorithm, and then uses if elseif
> statements to ensure the right optimisation procedure is called.
(Not having looked at the code) I would, instead of `:alg_a` use
`AlgTyp{:a}` and dispatch on it, i.e. the method described above.
> A second strategy would be the SmoothingKernels package which appears
> to have a different function name for each kernel.
This might be fine too. However, to write generic code one has to
resort to higher order functions which are not so performant at the
moment.
> So my question: Is there a preferred "julian" way of doing things, or is it
> a case of whatever seems to be the best fit on a case-by-case basis?
>
> Cheers,
>
> Colin
