Given the problems with Java's floating-point implementation [1], would this be reliable for statistical analysis?
2. The math in FastR, if I understand the presentation correctly, is performed by FORTRAN libraries anyway. Using battle tested FORTRAN libraries for matrix computations is common practice in C, Java, Julia, Matlab and most other environments. They basically all share the same underlying matrix math code.
Although it is standard in high-level systems to call out to a BLAS library [1], for some inexplicable reason it seems that both R and NumPy use the reference BLAS by default, which is quite slow – around 4x slower than better BLASes. Matlab ships with Intel's proprietary MKL, which includes a very fast BLAS implementation, while Julia ships with OpenBLAS, which is a similarly fast open source BLAS implementation derived from the legendary GotoBLAS [2]. Since all BLAS implementations share a common Fortran ABI, it's easy to swap them out, but it's not quite true that all of these systems are using the exact same Fortran code.
[1] https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprogra...
GNU R, for example, is implemented in C, and the implementations of R's basic arithmetic functions are actually quite complicated because they take care of so many of the edge cases cited in the cited post. For example, the round() function casts its argument first to a 64-bit before calling the C library's rint() function to preserve precision. [1]