All I was suggesting is that I prefer Distributions.jl to be an implementation of the distributions which have some sort of a closed-form characterization (this is obviously subjective, but let’s say you need at least a pdf and IID sampling; CDF/quantiles are optional, having at least a mean is nice when known in closed form, other moments as available).
So, to me, the ideal Distributions.jl is just code implementing formulas and tables usually found in the appendix of some classical stats textbook or a paper. The “basic” stuff, which at the same time can be difficult to program because of all the finicky details.
Many other things are distributions, ie basically any finite measure can be used to define one, and outcome of a simulation also defines a distribution. I find it great that people want to experiment with describing these things in a single unified conceptual framework, and I follow this with interest, but I don’t think that Distributions.jl should be used to hammer out these ideas.
Of course, it it needs to be changed just a bit to support these projects, that’s great and it should be done. But I don’t think that Distributions.jl is the right place to think about distributions over arbitrary objects, DSLs to describe them, generalized sampling, etc.