models
(Package: lsmeans) :
Models supported in pkg{lsmeans
Here we document what model objects may be used with lsmeans, and some special features of some of them. We start with those in the stats package; the other packages follow in alphabetical order.
Using a fitted model object, determine a reference grid for which least-squares means are defined. The resulting ref.grid object encapsulates all the information needed to calculate LS means and make inferences on them.
These functions return standard sets of contrast coefficients. The name of any of these functions (with the .lsmc omitted) may be used as the method argument in contrast, or as the contr argument or left-hand side of a spec formula in lsmeans.
lsmeans
(Package: lsmeans) :
Least-squares means (or predicted marginal means)
Compute least-squares means (predicted marginal means) for specified factors or factor combinations in a linear model, and optionally comparisons or contrasts among them.
Objects of class ref.grid or lsmobj contain several settings in their "misc" slot that affect primarily the defaults used by summary. This update method allows them to be changed more safely than by modifying this slot directly.
A reference grid encapsulates everything needed to compute least-squares means, independently of the underlying model object. The "lsmobj" class is a minor extension of "ref.grid" where the linear predictors for the reference grid are transformed in some linear way such as marginal averages or contrasts.
Create the needed information to perform transformations of the response variable, including inverting the transformation and estimating variances of back-transformed predictions via the delta method. make.tran is similar to make.link, but it covers additional transformations. The result can be used as an environment in which the model is fitted, or as the tran argument in update.ref.grid (when the given transformation was already applied in an existing model).
This package provides methods for obtaining so-called least-squares means for factor combinations in a variety of fitted linear models. It can also compute contrasts or linear combinations of these least-squares means, (several standard contrast families are provided), and in addition can estimate and contrast slopes of trend lines. Popular adjustments for multiple-comparisons are provided, as well as graphical ways of displaying the results.
providing 
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