# R package **emmeans**: Estimated marginal means

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

**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.Vignettes are provided on various aspects of EMMs and using the package. See the CRAN page

## Model support

The package incorporates support for many types of models, including standard models fitted using

`lm`

,`glm`

, and relatives, various mixed models, GEEs, survival models, count models, ordinal responses, zero-inflated models, and others. Provisions for some models include special modes for accessing different types of predictions; for example, with zero-inflated models, one may opt for the estimated response including zeros, just the linear predictor, or the zero model. For details, see`vignette("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 writing`recover_data`

and`emm_basis`

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

## Versions and installation

**CRAN**The latest CRAN version may be found at https://CRAN.R-project.org/package=emmeans. Also at that site, formatted versions of this package's vignettes may be viewed.**Github**To install the latest development version from Github, install the newest version (definitely 2.0 or higher) of the**devtools**package; then run

```
remotes::install_github("rvlenth/emmeans", dependencies = TRUE, build_opts = "")
### To install without vignettes (faster):
remotes::install_github("rvlenth/emmeans")
```

*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