Alexandre Kabla

I briefly tested cfunction on time arrays. Works fine too. Performance is good, although not too different from function wrappers.

These are code examples that seem to deliver what we need: ``` function DM5(p::Array{Symbol}, G::Expr) unpack_expr = Meta.parse(string(join(string.(p), ","), ",=params")) anonf = @eval ((t, params) -> begin $unpack_expr; $G; end)...

Serialization seems to be the standard approach for Serialization. There should not be issues with the OrderedDict either, as we just use a normal Vector now. https://docs.julialang.org/en/v1/stdlib/Serialization/

@moustachio-belvedere I like JuliaFormatter.jl. Could be useful, especially with the Vim plugin!

Good point re: default values in the model and the user setting one of them such that it clashes with the default value of the other. Best to make it...

I'm not saying that dimensionless parameters don't depend on the data - they would too. In the context of our rheological models, the fractional exponents are likely to be bounded...

I agree that when a complex function is called, this is probably insignificant. Maybe we should think more broadly about how to assist the user tracking how the optimisation evolves....

ps: one of the things to figure out is the precision of the Weeks algorithm for our application. It might be that Talbot remains a better option if the numbers...

Thanks for linking to #32 Do you have a good approach to test accuracy for representative functions? Each method seems to have its strengths and weaknesses, with an accuracy that...

Yes, a new release would be good so that the new doc is made available soon.