[R] custom loss function + nonlinear models

[Christian Mora]

Hi all;

I'm trying to fit a reparameterization of the
assymptotic regression model as that shown in
Ratkowsky (1990) page 96. 

Y~y1+(((y2-y1)*(1-((y2-y3)/(y3-y1))^(2*(X-x1)/(x2-x1))))/(1-((y2-y3)/(y3-y1))^2))

where y1,y2,y3 are expected-values for X=x1, X=x2, and
X=average(x1,x2), respectively.

I tried first with Statistica v7 by LS and
Gauss-Newton algorithm without success (no
convergence: predictors are redundant....). Then I
tried with the option CUSTOM LOSS FUNCTION and several
algorithms like Quasi-Newton, Simplex, Hookes-Jeeves,
among others. In all these cases the model converged
to some values for the parameters in it.

My question is (after searching the help pages) : Is
there such a thing implemented in R or can it be
easily implemented? In other words, is it possible to
define which loss function to use and the algorithm to
find the parameters estimates? 

Thanks
Christian