Could you elaborate on that? What about statistics do you feel makes it necessary to have 1-based indexing?

It's mostly about expectations. Users of statistical software, who may or may not be programmers by inclination, use 1-based indexing. The program they came from uses 1-based indexing (R, SAS, Stata, etc.). When you talk about data, you rarely talk about the 0th observation. It's just a recipe for errors to have to code switch between 0 and 1-based indexing.

I've used both 0 based (python, C++), and 1 based (R, matlab, mathematica), but I really belive 1 based is the right choice for Julia. It isn't necessary, but it feels better.

I am totally fine with zero base in Python and Lisp and such. BUT when I am doing statistics and the math is 1 based I think the ability to make a mistake is to big. Also for subset in R df[1, 1] would be the 2nd row and 2nd column would just throw most users of R.

The REPL is on par with ipython (perhaps even better). Also are some IDEs for it but they aren't quite there yet.

The only confusing edge case I've encountered is in biology, when manipulating a genomic region of zero length. Such a region is used, for example, when representing the location of an insertion of additional sequence. Before the insertion, the region has zero length. So, if the insertion occurs after the 500th element, then the zero-length range, in 1-based indexing, starts at 501 and ends at 500. However, it's rarely necessary to deal with this case directly, as such a range can be constructed as "starting at 501 with length 0".