Regression (Linear, Generalized Linear, Nonlinear Models) : lmrob() (robustbase) and lmRob() (robust) where the former uses the latest of the fast-S algorithms and heteroscedasticity and autocorrelation corrected (HAC) standard errors, the latter makes use of the M-S algorithm of Maronna and Yohai (2000), automatically when there are factors among the predictors (where S-estimators (and hence MM-estimators) based on resampling typically badly fail). The ltsReg() and lmrob.S() functions are available in robustbase, but rather for comparison purposes. rlm() from MASS. Note that Koenker's quantile regression package quantreg contains L1 (aka LAD, least absolute deviations)-regression as a special case, doing so also for nonparametric regression via splines. Package mblm 's function mblm() fits median-based (Theil-Sen or Siegel's repeated) simple linear models. Generalized linear models (GLMs) are provided both via glmrob() (robustbase) and glmRob() (robust). Robust Nonlinear model fitting is available through robustbase 's nlrob(). multinomRob fits overdispersed multinomial regression models for count data. rgam fits robust GAMs, i.e., robust Generalized Additive Models.


pls see the website:http://cran.r-project.org/web/views/Robust.html  

GM estimator:
sphet - A comprehensive library to estimate any kind of spatial model by GM using heteroskedasticity and autocorrelation consistent estimators of the VC matrix

谢谢指教

好贴！受教了！