Ok, so IIUC the audience is academic BUT is people interested in
using D as a means to an end, not computer scientists? I use D
for bioinformatics, which IIUC has similar requirements to
econometrics. From my point of view:
I'd emphasize the following:
Native efficiency. (Important for large datasets and monte carlo
simulations)
Garbage collection. (Important because it makes it much easier
to write non-trivial data structures that don't leak memory, and
statistical analyses are a lot easier if the data is structured
well.)
Ranges/std.range/builtin arrays and associative arrays. (Again,
these make data handling a pleasure.)
Templates. (Makes it easier to write algorithms that aren't
overly specialized to the data structure they operate on. This
can also be done with OO containers but requires more boilerplate
and compromises on efficiency.)
Disclaimer: These last two are things I'm the primary designer
and implementer of. I intentionally put them last so it doesn't
look like a shameless plug.
std.parallelism (Important because you can easily parallelize
your simulation, etc.)
dstats (https://github.com/dsimcha/dstats Important because a
lot of statistical analysis code is already implemented for you.
It's admittedly very basic compared to e.g. R or Matlab, but it's
also in many cases better integrated and more efficient. I'd say
that it has the 15% of the functionality that covers ~70% of use
cases. I welcome contributors to add more stuff to it. I
imagine economists would be interested in time series, which is
currently a big area of missing functionality.)
