Dear Ed,
thank you for your reply - it crossed my reply to Matthew, so I’ll
update my
statement
by saying that my question “why does Ed Borasky” like R is now answered
and
in
a very informative way.
I am a mathematician myself, and I’ll agree that in the field of
biological
modelling, where
I am working in, there’s a lot of statistics, and of course I knew of R
for
doing
statistics, and i like it so again: no criticism of R.
My point was just that what I like about R needn’t have any connection
with
what
you like about R - otherwise there’d one important reason less maintain
mailing lists.
For instance, I don’t agree with your point
Well, first of all, being a mathematician, I’ll make the claim that
any sufficiently large task, programming or otherwise, has some
statistics in it. :).
There’s graph theory, group theory, combinatorics ,…
If you do solving of polynomial equations in many variables, to model a
problem
of this field, you can use Groebner bases I was talking about in my
last
email.
The problem is that generically, the result you’ll get is of twice
exponential
complexity in the number of input variables, that is to say, you’ll
get to order of 2^(2^(n/2))) terms as a result, where n is the number of
input
variables as a worst case. This will eat up all your memory, no matter
how
much you have. A lot of work is going on to reduce that number , and
this
requires a lot of programming.
But I value your response and I’ll think about it in the near future,
when I
come
back to using R.
Best regards,
Axel