install.packages("devtools")
devtools::install_github("easystats/see")

library("see")

library(ggplot2)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) + 
    geom_point2() + theme_modern()

library(rstanarm)
library(estimate)

rstanarm::stan_glm(Sepal.Width ~ poly(Petal.Length, 2), data = iris) %>% 
    estimate::estimate_fit(keep_draws = TRUE, length = 100, draws = 250) %>% 
    estimate::reshape_draws() %>% ggplot(aes(x = Petal.Length, 
    y = Draw, group = Draw_Index)) + geom_line(color = "white", 
    alpha = 0.05) + scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 
    0)) + theme_blackboard()

p1 <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + 
    geom_boxplot() + theme_modern(axis.text.angle = 45) + scale_fill_material_d()

p2 <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + 
    geom_violin() + theme_modern(axis.text.angle = 45) + scale_fill_material_d(palette = "ice")

p3 <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) + 
    geom_point2() + theme_modern() + scale_color_material_c(palette = "rainbow")

plots(p1, p2, p3, ncol = 2)

plots(p1, p2, p3, ncol = 2, tags = paste("Fig. ", 1:3))

normal <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) + 
    geom_point(size = 8, alpha = 0.3) + theme_modern()

new <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) + 
    geom_point2(size = 8, alpha = 0.3) + theme_modern()

plots(normal, new, ncol = 2)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) + 
    geom_violindot(fill_dots = "black") + theme_modern() + scale_fill_material_d()

library(bayestestR)
library(rstanarm)

model <- rstanarm::stan_glm(Sepal.Length ~ Petal.Width * Species, 
    data = iris)

result <- estimate_density(model)

plot(result)

plot(result, stack = FALSE)

result <- p_direction(model)

plot(result) + theme_modern() + scale_fill_manual(values = c("red", 
    "green"))

how_to_plot(result)
## # Assuming that the input object is `x`:
## 
## data_plot(x) %>%
##   as.data.frame() %>%
##   ggplot(aes(x = x, y = y, height = height, group = y, fill = fill)) +
##   ggridges::geom_ridgeline_gradient() +
##   add_plot_attributes(x) +
##   geom_vline(aes(xintercept = 0))

library(rstanarm)

model <- rstanarm::stan_glm(Sepal.Length ~ Petal.Width * Species, 
    data = iris)
result <- hdi(model, ci = c(0.5, 0.75, 0.9, 0.95))

plot(result) + theme_modern() + scale_fill_brewer(palette = "Purples", 
    direction = -1)

result <- rope(model, ci = c(0.9, 0.95))

plot(result, data = model, rope_color = "red") + theme_modern() + 
    scale_fill_brewer(palette = "Greens", direction = -1)

model <- rstanarm::stan_glm(mpg ~ wt + gear + cyl + disp, data = mtcars)
result <- equivalence_test(model)

plot(result) + theme_blackboard() + scale_fill_material()

result <- equivalence_test(model, ci = c(0.9, 0.95))
plot(result) + theme_abyss() + scale_fill_flat()

result <- bayesfactor_savagedickey(model)

plot(result) + theme_modern() + scale_color_material() + scale_fill_material()

lm0 <- lm(qsec ~ 1, data = mtcars)
lm1 <- lm(qsec ~ drat, data = mtcars)
lm2 <- lm(qsec ~ wt, data = mtcars)
lm3 <- lm(qsec ~ drat + wt, data = mtcars)

result <- bayesfactor_models(lm1, lm2, lm3, denominator = lm0)

plot(result, n_pies = "one", value = "probability") + scale_fill_pizza(reverse = TRUE)

plot(result, n_pies = "many", value = "BF") + scale_fill_flat(palette = "rainbow", 
    reverse = TRUE)

library(rstanarm)
library(estimate)

model <- stan_glm(Sepal.Width ~ Species, data = iris) + theme_modern()

contrasts <- estimate_contrasts(model)
means <- estimate_means(model)

plot(contrasts, means)