[RsR] Robust regression

[Matias Salibian-Barrera]

Dear List,

A few days ago I uploaded the package "roblm" to CRAN. It implements 
MM-regression estimators and currently has some diagnostic plots as 
well. The documentation needs quite a bit of work, but the main 
information is there.

Note that the name of the package (and the corresponding class) is 
"roblm", but this does not mean that I necessarily prefer this name over 
others. I've been working on this for some time now, and for the reasons 
that I mentioned in Treviso ("rlm" and "lmRob" are already taken) I 
settled for roblm.

I took the liberty to re-organize the regression workgroup minutes 
relating to linear regression in a format closer to a "to-do" list. I 
would very much appreciate your feedback. Names within parenthesis 
indicate "volunteers" for a particular task. Please feel free to correct 
all my mistakes (and to add your name to the list of volunteers!)

Thanks.

Matias

-- An initial (partial) list of things that remain to be done for robust 
linear regression

- expose the "initial estimator" as a separate function.
	This should include the fast-S (already built-in), the
	alternate M-S estimator for the case of many factor
	variables (Victor can provide code), and the heuristic
	initial estimator of Yohai-Pena (Victor has code?)
- explore how to use score ("psi") functions declared in
	R (as S4 objects) in the C code
- decide which options should stay in the "control" function
- add to summary.roblm(), print.roblm() and
	print.summary.roblm() information on which estimation
	method was used (MM, etc)
- write a model selection function (Victor has code for a robust
	backward stepwise method (RFPE?); Elvezio may know of / have
	code for the robust Cp; Eva can provide this part?)
- write an "anova" function using robust F-, Wald tests	(Matias)
- add the robust weights to the returned object (Matias)
- improve / complete roblm documentation (Matias)
- incorporate more data sets (with documentation) (Matias)