What's wrong with statistics in Julia?

Re:What's wrong with statistics in Julia? by complicated on Saturday November 29, 2014 @08:13PM (#8675639)

Actually, I think your point rather showcases that Julia could be useful for the statistics community! Due to performance reasons you occasionally have to abandon R to use Numpy, but for everything else you use R. Thus, in a sense you still have the problem that Julia tries to solve: You constantly have to jump between two different languages depending on the problem size.

Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.

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Re:What's wrong with statistics in Julia? by vegabook on Saturday November 29, 2014 @08:44PM (#8675681)

Yes and yes. Julia is better because, having seen the mistakes of 3 x 20 year old languages, it is creating an (incrementally) better PythonRMatlab base language (not libraries). Had Julia dropped into my lap 5-10 years ago I would have been a fool to use anything else. The fact is though, it is very late to a party where everyone has already danced the night away, and just as the music is about to change (massively parallel multi node functional).

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Re:What's wrong with statistics in Julia? by sgt101 on Sunday November 30, 2014 @04:19AM (#8676422)

I think (this is gut - not an authority!) that Julia is very well placed for the massively parallel multi-node functional future of which you speak. lazy.jl and the cluster implementations such as spark.jl are evolving.

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