arthur-albuquerque

### Thanks to metafor-project.org 
### https://www.metafor-project.org/doku.php/plots:forest_plot_revman


### You will want to use png or pdf or tif to save this res 350, width=3196, height=1648


### Import Dataset
pacman::p_load(readxl,
               metafor,

arthur-albuquerque

library(ggplot2)
library(ggdist)
library(MetBrewer)
library(dplyr)

pal = met.brewer(name="VanGogh1", n=7, type="discrete")

set.seed(123)

arthur-albuquerque

pacman::p_load(dplyr,
               brms,
               patchwork,
               ggplot2)

# Install rmsb v0.2 to use cmdstan
pacman::p_load_gh("harrelfe/rmsb")

# Data

arthur-albuquerque

pacman::p_load(rmsb,
               dplyr,
               magrittr,
               ggdist,
               tidyr,
               brms)

# Data
a <- c(rep(0,0), rep(1,3), rep(2,5), rep(3,5), rep(4,25), rep(5,40), rep(6,24))
b <- c(rep(0,2), rep(1,3), rep(2,9), rep(3,17), rep(4,33), rep(5,18), rep(6,18))

arthur-albuquerque

pacman::p_load(survival, brms, simsurv, splines2) 

n <- 100

h = c(rep("A", n/4),
      rep("B", n/4),
      rep("C", n/4),
      rep("D", n/4))
      
covs <- data.frame(id  = 1:n, race_final = stats::rbinom(n, 1L, 0.5), 

arthur-albuquerque

pacman::p_load(extraDistr, dplyr, purrr, tidyr, ggplot2, forcats, ggdist, patchwork)

set.seed(123)

N = 100

mean_ARR = rhnorm(N, sigma = 0.02)

d = 
  tibble(mu = mean_ARR) |> 

arthur-albuquerque

pacman::p_load(rms, marginaleffects, brms, dplyr, tidybayes)

load(url(
  "https://github.com/vincentarelbundock/modelarchive/raw/main/data-raw/gusto.rda"
))

gusto <- subset(gusto, tx %in% c("tPA", "SK"))
gusto$tx <- factor(gusto$tx, levels = c("tPA", "SK"))

dd = sample_frac(gusto, size = 0.1, weight = tx)

arthur-albuquerque

# Install/load packages -----
pacman::p_load(ggdist,
               dplyr,
               tidyr,
               rmsb,
               stringr,
               gt,
               flextable)

## Data ----

arthur-albuquerque

pacman::p_load(brms,
               tidybayes,
               ggplot2,
               dplyr,
               tidyr,
               gt,
               stringr)

#define function to calculate mode
# https://www.statology.org/mode-in-r/

arthur-albuquerque

library(rms)

#define function to calculate mode
# https://www.statology.org/mode-in-r/
find_mode <- function(x) {
  u <- unique(x)
  tab <- tabulate(match(x, u))
  u[tab == max(tab)]
}