Robust Linear Mixed Effects Models

A method to fit linear mixed effects models robustly. Robustness is achieved by modification of the scoring equations combined with the Design Adaptive Scale approach.

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The R-package robustlmm provides functions for estimating linear mixed effects models in a robust way.

The main workhorse is the function rlmer; it is implemented as direct robust analogue of the popular lmer function of the lme4 package. The two functions have similar abilities and limitations. A wide range of data structures can be modeled: mixed effects models with hierarchical as well as complete or partially crossed random effects structures are possible. While the lmer function is optimized to handle large datasets efficiently, the computations employed in the rlmer function are more complex and for this reason also more expensive to compute. The two functions have the same limitations in the support of different random effect and residual error covariance structures. Both support only diagonal and unstructured random effect covariance structures.

The robustlmm package implements most of the analysis tool chain as is customary in R. The usual functions such as summary, coef, resid, etc. are provided as long as they are applicable for this type of models (see rlmerMod-class for a full list). The functions are designed to be as similar as possible to the ones in the lme4 package to make switching between the two packages easy.


This R-package is available on CRAN. Install it directly in R with the command


This package requires lme4 version at least 1.1 and other packages. Make sure to install them as well.

You can also install the package directly from github:

install.packages("devtools") ## if not already installed
install_github("robustlmm", "kollerma")


Reference manual

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2.3 by Manuel Koller, a year ago

Browse source code at

Authors: Manuel Koller

Documentation:   PDF Manual  

Task views: Robust Statistical Methods

GPL-2 license

Imports ggplot2, lattice, nlme, methods, robustbase, xtable, Rcpp, fastGHQuad

Depends on lme4, Matrix

Suggests digest, reshape2, microbenchmark

Linking to Rcpp, RcppEigen, robustbase, cubature

System requirements: C++11

Suggested by insight.

See at CRAN