# 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)
```