# $M$-estimation in multivariate linear regression model in R

Is there any function for $M$-estimation in multivariate linear regression model in R. I can estimate the $\beta$'s in my model by using the rlm() by rewriting the $y$-variables into one column but, I would like to use one function to get the $\beta$'s.

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Of possible interest: Link. – user10525 May 22 '12 at 11:19
@Procrastinator Your link does show someone who proves examples of robust estimation in R and specifically M-estimation. I think that qualifies as an answer to the question. Why don't you want to post it as an answer? – Michael Chernick May 22 '12 at 21:25
@MichaelChernick Thanks for your comment. I did not post it as an answer because I do not really understand what $M$-estimation means. I did a quick search by mere curiosity and found that link. – user10525 May 22 '12 at 21:29
It looks like that link gives examples of univariate regression models using robust ($M$) estimation, but I think the question is about $M$-estimation for a multivariate dependent variable. – Macro May 23 '12 at 13:59

The CRAN package rrcov specializes in robust estimation. The function covMest() gives the robust M-estimation of the covariance of a , but I could not find a specific function for multivariate regression.
If you already have a work around, you can write your own function to recode the data, call rml() and return the $\beta$s.