Which D features to emphasize for academic review article

[dsimcha]

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.)