ggcoef_model(
  model,
  tidy_fun = broom::tidy,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  show_p_values = TRUE,
  signif_stars = TRUE,
  return_data = FALSE,
  ...
)

ggcoef_compare(
  models,
  type = c("dodged", "faceted"),
  tidy_fun = broom::tidy,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  return_data = FALSE,
  ...
)

ggcoef_multinom(
  model,
  type = c("dodged", "faceted"),
  y.level_label = NULL,
  tidy_fun = broom::tidy,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  intercept = FALSE,
  include = dplyr::everything(),
  significance = 1 - conf.level,
  significance_labels = NULL,
  show_p_values = TRUE,
  signif_stars = TRUE,
  return_data = FALSE,
  ...
)

ggcoef_plot(
  data,
  x = "estimate",
  y = "label",
  exponentiate = FALSE,
  point_size = 2,
  point_stroke = 2,
  point_fill = "white",
  colour = NULL,
  colour_guide = TRUE,
  colour_lab = "",
  colour_labels = ggplot2::waiver(),
  shape = "significance",
  shape_values = c(16, 21),
  shape_guide = TRUE,
  shape_lab = "",
  errorbar = TRUE,
  errorbar_height = 0.1,
  errorbar_coloured = FALSE,
  stripped_rows = TRUE,
  strips_odd = "#11111111",
  strips_even = "#00000000",
  vline = TRUE,
  vline_colour = "grey50",
  dodged = FALSE,
  dodged_width = 0.8,
  facet_row = "var_label",
  facet_col = NULL,
  facet_labeller = "label_value"
)

# Small function to display plots only if it's interactive
p_ <- GGally::print_if_interactive

if (require(broom.helpers)) {
  data(tips, package = "reshape")
  mod_simple <- lm(tip ~ day + time + total_bill, data = tips)
  p_(ggcoef_model(mod_simple))

  # custom variable labels
  # you can use the labelled package to define variable labels before computing model
  if (require(labelled)) {
    tips_labelled <- tips %>%
      labelled::set_variable_labels(
        day = "Day of the week",
        time = "Lunch or Dinner",
        total_bill = "Bill's total"
      )
    mod_labelled <- lm(tip ~ day + time + total_bill, data = tips_labelled)
    p_(ggcoef_model(mod_labelled))
  }
  # you can provide custom variable labels with 'variable_labels'
  p_(ggcoef_model(
    mod_simple,
    variable_labels = c(
      day = "Week day",
      time = "Time (lunch or dinner ?)",
      total_bill = "Total of the bill"
    )
  ))
  # if labels are too long, you can use 'facet_labeller' to wrap them
  p_(ggcoef_model(
    mod_simple,
    variable_labels = c(
      day = "Week day",
      time = "Time (lunch or dinner ?)",
      total_bill = "Total of the bill"
    ),
    facet_labeller = label_wrap_gen(10)
  ))

  # do not display variable facets but add colour guide
  p_(ggcoef_model(mod_simple, facet_row = NULL, colour_guide = TRUE))

  # a logistic regression example
  d_titanic <- as.data.frame(Titanic)
  d_titanic$Survived <- factor(d_titanic$Survived, c("No", "Yes"))
  mod_titanic <- glm(
    Survived ~ Sex * Age + Class,
    weights = Freq,
    data = d_titanic,
    family = binomial
  )

  # use 'exponentiate = TRUE' to get the Odds Ratio
  p_(ggcoef_model(mod_titanic, exponentiate = TRUE))

  # display intercepts
  p_(ggcoef_model(mod_titanic, exponentiate = TRUE, intercept = TRUE))

  # customize terms labels
  p_(
    ggcoef_model(
      mod_titanic,
      exponentiate = TRUE,
      show_p_values = FALSE,
      signif_stars = FALSE,
      add_reference_rows = FALSE,
      categorical_terms_pattern = "{level} (ref: {reference_level})",
      interaction_sep = " x "
    ) +
    scale_y_discrete(labels = scales::label_wrap(15))
  )

  # display only a subset of terms
  p_(ggcoef_model(mod_titanic, exponentiate = TRUE, include = c("Age", "Class")))

  # do not change points' shape based on significance
  p_(ggcoef_model(mod_titanic, exponentiate = TRUE, significance = NULL))

  # a black and white version
  p_(ggcoef_model(
    mod_titanic, exponentiate = TRUE,
    colour = NULL, stripped_rows = FALSE
  ))

  # show dichotomous terms on one row
  p_(ggcoef_model(
    mod_titanic,
    exponentiate = TRUE,
    no_reference_row = broom.helpers::all_dichotomous(),
    categorical_terms_pattern =
      "{ifelse(dichotomous, paste0(level, ' / ', reference_level), level)}",
    show_p_values = FALSE
  ))

  # works also with with polynomial terms
  mod_poly <- lm(
    tip ~ poly(total_bill, 3) + day,
    data = tips,
  )
  p_(ggcoef_model(mod_poly))

  # or with different type of contrasts
  # for sum contrasts, the value of the reference term is computed
  if (require(emmeans)) {
    mod2 <- lm(
      tip ~ day + time + sex,
      data = tips,
      contrasts = list(time = contr.sum, day = contr.treatment(4, base = 3))
    )
    p_(ggcoef_model(mod2))
  }
}

if (require(broom.helpers)) {
  # Use ggcoef_compare() for comparing several models on the same plot
  mod1 <- lm(Fertility ~ ., data = swiss)
  mod2 <- step(mod1, trace = 0)
  mod3 <- lm(Fertility ~ Agriculture + Education * Catholic, data = swiss)
  models <- list("Full model" = mod1, "Simplified model" = mod2, "With interaction" = mod3)

  p_(ggcoef_compare(models))
  p_(ggcoef_compare(models, type = "faceted"))

  # you can reverse the vertical position of the point by using a negative value
  # for dodged_width (but it will produce some warnings)
## Not run: 
  p_(ggcoef_compare(models, dodged_width = -.9))

## End(Not run)
}

# specific function for nnet::multinom models
if (require(broom.helpers) && require(nnet)) {
  data(happy)
  mod <- multinom(happy ~ age + degree + sex, data = happy)
  p_(ggcoef_multinom(mod, exponentiate = TRUE))
  p_(ggcoef_multinom(mod, type = "faceted"))
  p_(ggcoef_multinom(
    mod, type = "faceted",
    y.level = c(
      "pretty happy" = "pretty happy\n(ref: very happy)",
      "very happy" = "very happy"
    )
  ))
}