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I'm curious for others thoughts on what to use for complex statistics if you needed high performance/speed.

Running an R script on a server to process data isn't efficient, but does that mean you have to roll your own stats package if you want to have a Java (for example) back-end?

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Not sure how complex your use case is, but I've found Pandas (on Python) to be just as powerful and much more performant than R for working with scientific data. It's built on Numpy so you can use Scipy's statistical functions with it seamlessly.

Recent benchmarks show the performance of pandas, data.table and dplyr to be pretty similar, with data.table usually being the fastest.

Here's a benchmark Matt recently did comparing data.table, dplyr and pandas on 50GB and 100GB: https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A...

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