R help - May 2005 - Fitting Data with errors to non-polynomial Linear Model

Dear R-help,
After two days of search on the archive of your web-site, I found partial 
answers to the problem that I want to solve, but this is not concluent to me
and I am thinking that perhaps someone could answer exactly my problem:
I have a theoretical Model (for the prediction of some physical quantity) which
can be seen as a linear Model with 2 parameters. On the other side the measured
data concerning the physical quantity in question, and that I have to my
disposition, is a set of
20 points with both errors in x and y, the x error being the same for 
each point. I would like to fit my model to the data and find the corresponding 
values of the parameters in order to make predictions for some other physical
quantities that depend on the parameters.

My theoretical Model which has 2 free parameters a,b and one independant
variable X, is
 of the type:
f(X;a,b;M,N,P)=a*f1(X;M)+b*f2(X;M,N)+f3(X;M,N,P)
where f1 and f2 are partial fractions and f3 is a complicated non-linear
function of X
and M, which is bipartite in the range of X on which I want to make my fit. 
M, N et P are constants with errors (physical quantities that enter into the
equation
of the model).
I do not know R very well and my questions are:
1.Is there a R-package where it is possible to define arbitrary basis functions 
for a linear Model?
2.The error in y can be taken into acount with the weight option, but what about
the
error in X? (and in M, N and P)
3.Is it possible, with R, to handle with my bipartite fonction f3 simply? (with
the
help of the unitstep function or something like that..?)
I know that the error in X can be implemented by what is called "effective 
variances technique", but is it available in some package?
Many thanks for your help by advance.
Samuel
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