I've been doing a lot of work behind the scenes there, but it's still going to take a while to offer a mature product. What we really need are more qualified engineers to speed up the process, but there's a vanishingly small group of people who are interested in doing eng work on statistics libraries.
A lot of interesting things are going on in the DataFrames area - especially with the recent work on NullableArrays by David Gold and John Myles White. A blog post will be out soon describing it all. In my opinion, Escher.jl makes it really easy to build amazing dashboards and is progressing well.
But, a lot more remains to be done, and contributions would be really welcome in these areas that take Julia's statistical computing to the next level.
I'd like to add a note about my experience with doing this; I ended up forking Distributions.jl and added a fitting function for the Weibull distribution. Still need to clean it up and do a pull request,.
The amazing thing about Julia I've come to enjoy is how clean the source code, how easy it is to find functionality, and it's all written IN Julia. That means I can modify libraries that are normally C or C++ for perf reasons. It's changed the prospect of needing to add functionality from one of hesitation to feeling that it's actually enjoyable.
I'm really glad to hear this! I'd like to think that the community and inspectability argument for Julia is actually one of the strongest ones out there, but it's not easy convincing people who haven't had that experience.