On Tuesday, 17 March 2015 at 21:00:11 UTC, bachmeier wrote:
What do you have in mind? I no longer work much with Python so
my knowledge is limited, but calling D from R or Julia should
be no different from calling C from those languages, as you
normally compile your C code into a shared library anyway.
I've done the R->D thing many times and in the process have
worked with a big chunk of the R API. Things like allocating R
data structures from a D function, adding assertions to your D
code to allow for an easy exit when things don't work out, and
calling functions in the R math library, among other things,
are not difficult.
(For work at least) I find myself in situations where I'm forced
into using loops in R or Python. Sometimes the loops can be
parallelized, but not always. Python is not good for
parallelizing, and R is only good on a Linux machine (which I
can't use at work). Both are bad when you really have to do a
loop. Julia is better for that, but I can't get the package
manager to work on my work computer because of a corporate
firewall. Anyway, all the calculations I would be doing would
involve matrix math/regressions/optimization, most of which could
be done in C++ (so being able to call C++ libraries would go a
long way).
I've looked at calling C code from R before and using Cython in
Python. However, I think it's another step altogether to get an R
library (like lme4) to work in a D program. I have zero idea how
to go about doing that. It has seemed like a bit of a hassle for
anything complex. For instance, suppose I wanted to create a loop
in C that calls an R function from lme4 multiple times and then I
wanted to call that C function from R. Makes my head hurt just
thinking about it.
Nevertheless, I think that there would be a lot of value in
writing up what you have done with R and D. That sounds very
interesting.
