Koalha

## Bayesian p-values!
library(tidyverse)
library(brms)
library(posterior)
library(tidybayes)
library(glue)
library(ggview)
options(scipen = 999)
set.seed(25)

Koalha

## Berkson's paradox or collider bias in surveys
library(tidyverse)
library(ggdist)
library(ggview)
library(dagitty)
library(ggdag)

theme_set(theme_minimal(base_family = "Karla"))

## A dag of the scenario

Koalha

## You can use the fill aesthetic to indicate indicate uncertainty levels in ggdist geoms!
library(tidyverse)
library(tidybayes)
library(posterior)
library(brms)
library(scico)
library(marginaleffects)
library(ggview)

set.seed(5) #for reproducibility

Koalha

## Visualize uncertainty in bar charts using fuzzy class boundaries
## as in https://stats.andrewheiss.com/lemon-lucifer/analysis/paper.html
library(tidyverse)
library(tidybayes)
library(ggview)
library(patchwork)
library(glue)
library(scico)

Koalha

library(tidyverse)
library(brms)
library(tidybayes)
options(mc.cores = min(parallel::detectCores(),4))

## Simulate data
N <- 1000

dat <- tibble(
  y = sample(LETTERS[1:4], replace=TRUE, size = N) |>