# Robust linear mixed effects models

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.

## Installation

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

`install.packages("robustlmm")`

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
require(devtools)
install_github("kollerma/robustlmm")
require(robustlmm)
```