# R package **emmeans**: Estimated marginal means (least-squares means)

#### Note

**emmeans** is a continuation of the package **lsmeans**. The latter will eventually be retired.

## Features

Estimated marginal means (EMMs, previously known as least-squares means in the context of traditional regression models) are derived by using a model to make predictions over a regular grid of predictor combinations (called a *reference grid*). These predictions may possibly be averaged (typically with equal weights) over one or more of the predictors. Such marginally-averaged predictions are useful for describing the results of fitting a model, particularly in presenting the effects of factors. The **emmeans** package can easily produce these results, as well as various graphs of them (interaction-style plots and side-by-side intervals).

- Estimation and testing of pairwise comparisons of EMMs, and several other types of contrasts, are provided. There is also a
`cld`

method for display of grouping symbols.
- Two-way support of the
`glht`

function in the **multcomp** package.
- For models where continuous predictors interact with factors, the package’s
`emtrends`

function works in terms of a reference grid of predicted slopes of trend lines for each factor combination.
- Incorporates support for many types of models, including those in
**stats** package (`lm`

, `glm`

, `aov`

, `aovlist`

), linear and generalized linear mixed models (e.g., **nlme**, **lme4**, **afex**), ordinal-response models (e.g., **ordinal**, **MASS**), survival analysis (e.g., **survival**, **coxme**), generalized estimating equations (**gee**, **geepack**), and others. See `help("models", package = "emmeans")`

- Various Bayesian models (
**carBayes**, **MCMCglmm**, **MCMCpack**) are supported by way of creating a posterior sample of least-squares means or contrasts thereof, which may then be examined using tools such as in the **coda** package.
Package developers may provide **emmeans** support for their models by providing `recover_data`

and `emm_basis`

methods. See `vignette("extending", package = "emmeans")`

To install the latest development version from Github, install the newest version of the **devtools** package; then run

```
devtools::install_github("rvlenth/emmeans", dependencies = TRUE,
build_vignettes = TRUE)
```

*Note:* If you are a Windows user, you should also first download and install the latest version of `Rtools`

.

For the latest release notes on this development version, see the NEWS file