When I last looked at Julia, the language wasn't yet stable. It's not fun developing packages for a language that changes from release to release, so I don't expect their ecosystem to stand a chance until they get to v1.0. All the advantages for package developers listed in this article are moot while the language remains a moving target.

It was said that v1.0 was due in early/mid 2017, but it looks like it's still a ways off.

There is (or was) a window of opportunity for Julia to steal mindshare in academia from MATLAB and R, but it feels like Python is beating them to the punch. Of course, Python had a 21 year head start.

Talking to people in the community my impression was that 1.0 would appear when 1.0 appeared and that they felt that given the difficulties faced by other languages and the work required to produce a language that was fit for purpose in the modern context, a gestation time of more than a couple of years was reasonable.

In terms of community I think that the economics community and private equity firms in particular seem to have picked Julia up enthusiastically.

Having said that, I suspect that it will be three years before Julia becomes mainstream, and then it will be a minority choice for five or six more - if it is the big success it deserves to be!

I was going to add that Python has been "getting there" since 1995, but I see you have already edited you comment :-)

There are over 1500 packages (pkg.julialang.org)in Julia now, with the ability to call Python and R, and any C and Fortran library written. Julia packages tend to be more Julian of course, and natural if you are using Julia.

I say most of the basic stuff is there, but a few things remain. What kinds of things are you looking for that Julia doesn't have?

What makes you say it lacks critical mass? I don't think that's accurate, but of course it takes time. Implementation time is important, yes - but julia is not just a fast-to-run-language, which is essentially the point - it is a fast-to-implement language.

At least in the field of computational biology most people use R or python because of the wealth of statistical packages and biology-specific packages (biomaRt) that makes implementing and testing models on biological data much faster than in Julia.

It's always this problem, if everybody is using R, it is hard to migrate to Julia. But in order to migrate to Julia you need people to stop using R and switch to Julia.

Is there no Julia wrapper around biomaRt? Would it be challenging to create and maintain?

(I don't mean to imply that _you_ should be the one to do it - I'm genuinely curious)