knitr, MASS, rmarkdown, rstanarm, snakecase

dplyr (>= 0.7.1), effects (>= 4.0-0), ggplot2 (>= 2.2.1),
magrittr, prediction (>= 0.2.0), purrr, rlang, scales,
sjlabelled (>= 1.0.5), sjmisc (>= 2.6.2), sjstats (>= 0.13.0),
snakecase, tibble (>= 1.3.3), tidyr (>= 0.7.0), tidyselect

Compute marginal effects at the mean or average marginal effects from
statistical models and returns the result as tidy data frames. These
data frames are ready to use with the 'ggplot2'-package.
Marginal effects can be calculated for many different models. Interaction
terms, splines and polynomial terms are also supported. The two main
functions are ggpredict() and ggaverage(), however, there are
some convenient wrapper-functions especially for polynomials or
interactions. There is a generic plot()-method to plot the results
using 'ggplot2'.
dplyr (>= 0.7.1), effects (>= 4.0-0), ggplot2 (>= 2.2.1),
magrittr, prediction (>= 0.2.0), purrr, rlang, scales,
sjlabelled (>= 1.0.6), sjmisc (>= 2.6.3), sjstats (>= 0.14.0),
tibble (>= 1.3.3), tidyr (>= 0.7.0), tidyselect

dplyr (>= 0.7.1), effects (>= 4.0-0), ggplot2 (>= 2.2.1),
magrittr, prediction (>= 0.2.0), purrr, rlang, scales,
sjlabelled (>= 1.0.8), sjmisc (>= 2.7.0), sjstats (>= 0.14.1),
tibble (>= 1.3.3), tidyr (>= 0.7.0), tidyselect

dplyr (>= 0.7.1), effects (>= 4.0-0), ggplot2 (>= 2.2.1),
magrittr, prediction (>= 0.2.0), purrr, rlang, scales,
sjlabelled (>= 1.0.8), sjmisc (>= 2.7.0), sjstats (>= 0.14.3),
tibble (>= 1.3.3), tidyr (>= 0.7.0), tidyselect

glmmTMB, knitr, lme4, MASS, nlme, rmarkdown, rstantools,
survey, testthat

dplyr, effects (>= 4.0-0), ggplot2, magrittr, prediction,
purrr, rlang, scales, sjlabelled (>= 1.0.12), sjmisc (>=
2.7.3), sjstats (>= 0.16.0), tibble, tidyr, tidyselect

Compute marginal effects from statistical models and returns the
result as tidy data frames. These data frames are ready to use with the
'ggplot2'-package. Marginal effects can be calculated for many different
models. Interaction terms, splines and polynomial terms are also supported.
The two main functions are ggpredict() and ggeffect(). There is a generic
plot()-method to plot the results using 'ggplot2'.
crayon, dplyr, ggplot2, lme4, magrittr, MASS, prediction,
purrr, rlang, scales, sjlabelled (>= 1.0.14), sjmisc (>=
2.7.5), sjstats (>= 0.17.1), tidyr
effects (>= 4.0-0), glmmTMB, knitr, nlme, pscl, rmarkdown,
rstanarm, rstantools, sandwich, survey, survival, testthat