R help - Sep 2006 - linear terms within a nonlinear model

I have a complicated nonlinear function, myfun(a,b,c),
that I want to fit to data, allowing one or more of the parameters
a, b, and c in turn to have linear dependence on other covariates.
In other words, I'd like to specify something like

nls(y~myfun(a,b,c),linear=list(a~f1,b~1,c~1))

  I know would this work in nlme *if I wanted to specify
random effects as well*, but I don't -- and wasn't able
to figure out how to specify a "null" random effect.
(Have looked in Pinheiro and Bates but haven't yet found
a solution ...)
I don't see how to do it in nls() or nlsList(), short of
implementing the linear structure within myfun().  
  Looked at Jim Lindsey's gnlm package but haven't yet
been able to figure it out.
   Does anyone have any ideas or tips?

  thanks
    Ben Bolker

Hi,

the contributed package 'drc' allows specification of non-linear
regression models with
individual parameter models that include covariates.

For an example see section 8 the accompanying paper in J. Statist. Software 
(http://www.jstatsoft.org/v12/i05/v12i05.pdf).

Christian