Robust (or "resistant") methods for statistics modelling have been available in S from the very beginning in the 1980s; and then in R in package stats. Examples are median(), mean(*, trim =. ), mad(), IQR(), or also fivenum(), the statistic behind boxplot() in package graphics) or lowess() (and loess()) for robust nonparametric regression, which had been complemented by runmed() in 2003. Much further important functionality has been made available in recommended (and hence present in all R versions) package MASS (by Bill Venables and Brian Ripley, see the book Modern Applied Statistics with S ). Most importantly, they provide rlm() for robust regression and cov.rob() for robust multivariate scatter and covariance.

This task view is about R add-on packages providing newer or faster, more efficient algorithms and notably for (robustification of) new models.

Please send suggestions for additions and extensions to the task view maintainer .

An international group of scientists working in the field of robust statistics has made efforts (since October 2005) to coordinate several of the scattered developments and make the important ones available through a set of R packages complementing each other. These should build on a basic package with "Essentials", coined robustbase with (potentially many) other packages building on top and extending the essential functionality to particular models or applications. Further, there is the quite comprehensive package robust, a version of the robust library of S-PLUS, as an R package now GPLicensed thanks to Insightful and Kjell Konis. Originally, there has been much overlap between 'robustbase' and 'robust', now robust depends on robustbase, the former providing convenient routines for the casual user where the latter will contain the underlying functionality, and provide the more advanced statistician with a large range of options for robust modeling.

We structure the packages roughly into the following topics, and typically will first mention functionality in packages robustbase and robust.

• 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. Quantile regression (and hence L1 or LAD) for mixed effect models, is available in package lqmm, whereas an MM-like approach for robust linear mixed effects modeling is available from package robustlmm. 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.
• Multivariate Analysis : Here, the rrcov package which builds (" Depends ") on robustbase provides nice S4 class based methods, more methods for robust multivariate variance-covariance estimation, and adds robust PCA methodology. It is extended by rrcovNA, providing robust multivariate methods for for incomplete or missing ( NA) data, and by rrcovHD, providing robust multivariate methods for High Dimensional data. Here, robustbase contains a slightly more flexible version, covMcd() than robust 's fastmcd(), and similarly for covOGK(). OTOH, robust 's covRob() has automatically chosen methods, notably pairwiseQC() for large dimensionality p. Package robustX for experimental, or other not yet established procedures, contains BACON() and covNCC(), the latter providing the neighbor variance estimation (NNVE) method of Wang and Raftery (2002), also available in covRobust. mvoutlier (building on robustbase) provides several methods for outlier identification in high dimensions. RSKC provides R obust S parse K -means C lustering. FRB performs robust inference based on F ast and R obust B ootstrap on robust estimators, including multivarate regression, PCA and Hotelling tests. covRobust provides the nearest neighbor variance estimation (NNVE) method of Wang and Raftery (2002). Note that robust PCA can be performed by using standard \R's princomp(), e.g., X <- stackloss; pc.rob <- princomp(X, covmat= MASS::cov.rob(X)) See also the CRAN task views Multivariate and Cluster
• Large Data Sets : ... (See also the CRAN task view MachineLearning.)
• Descriptive Statistics / Exploratory Data Analysis : ...
• Time Series :
  • Package robfilter contains robust regression and filtering methods for univariate time series, typically based on repeated (weighted) median regressions.
  • Peter Ruckdeschel has started to lead an effort for a robust time-series package, see robust-ts on R-Forge.
  • Further, robKalman, "Routines for Robust Kalman Filtering --- the ACM- and rLS-filter" , is being developed, see robkalman on R-Forge.
  Note however that these (last two items) are not yet available from CRAN.
• Econometric Models : Econometricians tend to like HAC (heteroscedasticity and autocorrelation corrected) standard errors. For a broad class of models, these are provided by package sandwich. Note that vcov(lmrob()) also uses a version of HAC standard errors for its robustly estimated linear models. See also the CRAN task view Econometrics
• Robust Methods for Bioinformatics : There are several packages in the Bioconductor project providing specialized robust methods. In addition, RobLoxBioC provides infinitesimally robust estimators for preprocessing omics data.
• Robust Methods for Survival Analysis : Package coxrobust provides robust estimation in the Cox model.
• Robust Methods for Surveys : On R-forge only, package rhte provides a robust Horvitz-Thompson estimator.
• Other approaches to robust and resistant methodology :
  • The package distr and its several child packages also allow to explore robust estimation concepts, see e.g., distr on R-Forge.
  • Notably, based on these, the project robast aims for the implementation of R packages for the computation of optimally robust estimators and tests as well as the necessary infrastructure (mainly S4 classes and methods) and diagnostics; cf. M. Kohl (2005). It includes the R packages RandVar, RobAStBase, RobLox, RobLoxBioC, RobRex. Further, ROptEst, and ROptRegTS.
  • RobustAFT computes Robust Accelerated Failure Time Regression for Gaussian and logWeibull errors.
  • wle Weighted Likelihood Estimation provides robustified likelihood estimation for a range of models, notably (generalized) regression, and time series (AR and fracdiff).