estimability (>= 1.3), graphics, methods, stats, utils, plyr,
mvtnorm, xtable (>= 1.8-2)
bayesplot, brms, car, coda (>= 0.17), ggplot2, lattice,
mediation, multcomp, multcompView, nlme, ordinal, pbkrtest (>=
0.4-1), lme4, lmerTest (>= 2.0.32), MASS, rsm, knitr,
rmarkdown, testthat

brms, estimability (>= 1.3), ggplot2, graphics, methods,
stats, utils, nlme, coda (>= 0.17), multcomp, plyr, mvtnorm,
xtable (>= 1.8-2)

bayesplot, biglm, brms, car, coda (>= 0.17), ggplot2,
lattice, mediation, multcomp, multcompView, nlme, ordinal,
pbkrtest (>= 0.4-1), lme4, lmerTest (>= 2.0.32), MASS, rsm,
knitr, rmarkdown, testthat

bayesplot, biglm, brms, car, coda (>= 0.17), ggplot2,
lattice, mediation, mgcv, multcomp, multcompView, nlme, ordinal
(>= 2014.11-12), pbkrtest (>= 0.4-1), lme4, lmerTest (>=
2.0.32), MASS, rsm, knitr, rmarkdown, testthat
CARBayes, coxme, gee, geepack, MCMCglmm, MCMCpack, nnet,
pscl, rstanarm, survival

estimability (>= 1.3), graphics, methods, numDeriv, stats,
utils, plyr, mvtnorm, xtable (>= 1.8-2)
bayesplot, biglm, brms, car, coda (>= 0.17), ggplot2,
lattice, mediation, mgcv, multcomp, multcompView, nlme, ordinal
(>= 2014.11-12), pbkrtest (>= 0.4-1), lme4, lmerTest (>=
2.0.32), MASS, MuMIn, rsm, knitr, rmarkdown, testthat
CARBayes, coxme, gee, geepack, MCMCglmm, MCMCpack, mice,
nnet, pscl, rstanarm, sommer, survival

bayesplot, biglm, brms, car, coda (>= 0.17), ggplot2,
lattice, mediation, mgcv, multcomp, multcompView, nlme, ordinal
(>= 2014.11-12), pbkrtest (>= 0.4-1), lme4, lmerTest (>=
2.0.32), MASS, MuMIn, rsm, knitr, rmarkdown, scales, testthat

Obtain estimated marginal means (EMMs) for many linear, generalized
linear, and mixed models. Compute contrasts or linear functions of EMMs,
trends, and comparisons of slopes. Plots and other displays.
Least-squares means are discussed, and the term "estimated marginal means"
is suggested, in Searle, Speed, and Milliken (1980) Population marginal means
in the linear model: An alternative to least squares means, The American
Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.