[R] D(dnorm...)?

Bill.Venables@csiro.au Bill.Venables at csiro.au
Thu Jan 26 08:56:50 CET 2006

While symbolic computation is handy, I actually think a more pressing
addition to R is some kind of automatic differentiation facility,
particularly 'reverse mode' AD, which can be spectacular.  There are
free tools available for it as well, though I don't know how well
developed they are.  See:


I admit this is not quite the same thing, but for statistical
computations this is, in my experience, the key thing you need.  (Well,
for frequentist estimation at any rate...)

There are commercial systems that use this idea already, of course.  Two
that I know of are 'ADMB' (and its associated 'ADMB-RE' for random
effects) estimation and of course the 'S-NUOPT' module for another
system not unlike R.

ADMB is, frankly, difficult to use but it performs so well and so
quickly once you get it going nothing else seems to come close to it.  I
has become almost a de-facto standard at the higher end of the fishery
stock assessment game, for example, where they are always fitting huge,
highly complex and very non-linear models.

Bill V.

-----Original Message-----
From: Berwin A Turlach [mailto:berwin at bossiaea.maths.uwa.edu.au] On
Behalf Of Berwin A Turlach
Sent: Thursday, 26 January 2006 4:50 PM
To: Spencer Graves
Cc: Venables, Bill (CMIS, Cleveland); r-help at stat.math.ethz.ch;
gunter.berton at gene.com; ripley at stats.ox.ac.uk
Subject: Re: [R] D(dnorm...)?

G'day Spencer,

>>>>> "SG" == Spencer Graves <spencer.graves at pdf.com> writes:

    SG> I'm not qualified to make this suggestion since I'm incapable
    SG> of turning it into reality, [...]
This statement applies to me too, nevertheless I would like to point
out the following GPL library:


I am wondering since some time how hard it would be to incorporate
that library into R and let it provide symbolic differentiation
capabilities for R.



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