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.

In rank-deficient models, the estimability of predictions is checked, to avoid outputting results that are not uniquely defined.

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.

We try to provide flexible (but pretty basic) graphics support for the

`emmGrid`

objects produced by the package. Also, support is provided for nested fixed effects.Response transformations and link functions are supported via a

`type`

argument in many functions (e.g.,`type = "response"`

to back-transform results to the response scale). Also, a`regrid()`

function is provided to reconstruct the object on any transformed scale that the user wishes.Two-way support of the

`glht`

function in the**multcomp**package.

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 are encouraged to incorporate

**emmeans**support for their models by writing`recover_data`

and`emm_basis`

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

**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

The developer of **emmeans** continues to maintain and occasionally add new features. However, none of us is immortal; and neither is software. I have thought of trying to find a co-maintainer who could carry the ball once I am gone or lose interest, but the flip side of that is that the codebase is not getting less messy as time goes on – why impose that on someone else? So my thought now is that if at some point, enough active R developers want the capabilities of **emmeans** but I am no longer in the picture, they should feel free to supersede it with some other package that does it better. All of the code is publicly available on GitHub, so just take what is useful and replace what is not.

`lsmeans()`

function itself is part of