[jordanb]

R is an open source version of S, which was a competitor to SAS.

Julia, from when I looked at it years ago was trying like a new version of Matlab or Mathematica. It was very linear-algebra focused, and were trying to replace those packages plus Fortran. They had some gimmicks like an IDE that would render mathematical notion like TeX for your matrices.

Python wasn't the obvious "Fortran killer" scientific language it is today. In fact it's arguably really weird that Python ended up winning that segment. In any case, I think Julia's been struggling since its inception.

[wdkrnls]

R and S are also very linear algebra focused. R developers just try to make C++ behave like R as much as possible when they need more speed. Hence, Rcpp. Otherwise, we prefer our LISPy paradise.

[tomrod]

I was in Austin while Travis Oliphant's wave from numpy led to Anaconda. After that we got to bring them in as consultants. It was wild talking to the team and hearing the inside-track dev info. It isn't a surprise to me that Python, as flexible and glue code as it is, became the Excel language of Scientific Computing.

[nequo]

What kinds of things did you hear from them?

[tomrod]

Mostly the vision and ideals which became Anaconda, conda, and miniconda, as well as the translation of ideas to use cases to implementations, and some ideas that came about later in other forms or libraries (numba, pytorch).

Basically a mini/beta/in-progress version of Pycon each week.