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