Imports: dplyr (>= 0.7.1), effects, ggplot2 (>= 2.2.1), magrittr, MASS,

Imports: dplyr (>= 0.7.1), effects, ggplot2 (>= 2.2.1), magrittr,
Matrix, prediction (>= 0.2.0), purrr, rlang, scales, sjlabelled
Suggests: knitr, MASS, rmarkdown, rstanarm

magrittr, prediction (>= 0.2.0), purrr, rlang, scales,
Suggests: knitr, MASS, rmarkdown, rstanarm, snakecase

snakecase, tibble (>= 1.3.3), tidyr (>= 0.7.0), tidyselect
Suggests: knitr, MASS, rmarkdown, rstantools

functions are ggpredict() and ggaverage(), however, there are
interactions. There is a generic plot()-method to plot the results
tibble (>= 1.3.3), tidyr (>= 0.7.0), tidyselect

Suggests: glmmTMB, knitr, lme4, MASS, nlme, rmarkdown, rstantools,

Imports: 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

The two main functions are ggpredict() and ggeffect(). There is a generic
plot()-method to plot the results using 'ggplot2'.
Imports: crayon, dplyr, ggplot2, lme4, magrittr, MASS, prediction,
purrr, rlang, scales, sjlabelled (>= 1.0.14), sjmisc (>=
Suggests: effects (>= 4.0-0), glmmTMB, knitr, nlme, pscl, rmarkdown,
rstanarm, rstantools, sandwich, survey, survival, testthat

Suggests: brms, effects (>= 4.0-0), emmeans, glmmTMB, knitr, nlme,
pscl, rmarkdown, rstanarm, rstantools, sandwich, survey,

Imports: dplyr, insight, magrittr, MASS, purrr, rlang, scales,
gee, ggplot2, GLMMadaptive, glmmTMB, knitr, lme4, Matrix,
MCMCglmm, mgcv, nlme, ordinal, prediction, pscl, rmarkdown,
rstanarm, rstantools, rstudioapi, sandwich, sjstats, survey,

Imports: graphics, insight (>= 0.11.0), MASS, sjlabelled (>= 1.1.2),
magrittr, Matrix, mice, MCMCglmm, mgcv, nlme, ordinal,
prediction, pscl, quantreg, rmarkdown, rms, robustbase,
rstanarm, rstantools, sandwich, see, sjstats, sjmisc (>=
2.8.2), survey, survival, testthat, VGAM

Description: Compute marginal effects and adjusted predictions from statistical
models and returns the result as tidy data frames. These data frames are
ready to use with the 'ggplot2'-package. Effects and predictions can be
calculated for many different models. Interaction terms, splines and
polynomial terms are also supported. The main functions are ggpredict(),
ggemmeans() and ggeffect(). There is a generic plot()-method to plot the
Imports: graphics, insight (>= 0.13.0), MASS, sjlabelled (>= 1.1.2),
emmeans (>= 1.4.1), gam, gamlss, gamm4, gee, geepack, ggplot2,
GLMMadaptive, glmmTMB (>= 1.0.0), gridExtra, haven, httr,
knitr, lme4, logistf, magrittr, margins, Matrix, mice,
MCMCglmm, mgcv, nlme, ordinal, parameters, prediction, pscl,
sandwich, see, sjstats, sjmisc (>= 2.8.2), survey, survival,