Removing MonteCarloMeasurements support about arviz.jl HOT 2 CLOSED

I'm not using ArviZ either so I think it's fine to remove the support.

The

Julia needs to know whether particles should be treated as a scalar (and so broadcasting over its elements) or as an array

problem has been "solved" by no longer trying to be "smart" and do what the user might want, and instead adopt the filosophy that Particles are always scalars, and any function that operates on the individual samples have been given a new name. Having said that, I understand why Particles are a suboptimal fit for Soss, but it was fun exploring the limits of the approach :)

from arviz.jl.

Thanks @sethaxen . I really like the ideas introduced in MCM, and they've had a big influence on how I think about manipulating samples. One issue is that for any operation on Particles, Julia needs to know whether particles should be treated as a scalar (and so broadcasting over its elements) or as an array. Also, it's very easy for particles to get "out of sync", since each is passed around individually. It's of course possible to put them in a container, but there's no container I know of that makes this very natural.

Recently I've moved toward instead representing samples in terms of a TupleVector. Each row is a named tuple, but it's also easy to access these column-wise. For particle-like manipulations, I have a @with macro that lets you treat a TupleVector as a kind of namespace to map over.

This is all a long-winded way (sorry) to say I really appreciate @baggepinnen's contributions in MCM, but I'm not currently using it.

from arviz.jl.