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Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1(3), 227-235.

Berkey, C. S., Hoaglin, D. C., Antczak-Bouckoms, A., Mosteller, F., & Colditz, G. A. (1998). Meta-analysis of multiple outcomes by regression with random effects. Statistics in Medicine, 17(22), 2537-2550.

van Houwelingen, H. C., Arends, L. R., & Stijnen, T. (2002). Advanced methods in meta-analysis: Multivariate approach and meta-regression. Statistics in Medicine, 21(4), 589-624.

Nam, I.-S., Mengersen, K., & Garthwaite, P. (2003). Multivariate meta-analysis. Statistics in Medicine, 22(14), 2309-2333.

Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39-65.

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genjournal <- function() {
   x1 <- sample(c("American Journal of", "Austin Journal of", "Annals of", "Archives of", "International Journal of", "Journal of", "Open Journal of", "World Journal of", "Insights Journal of", "Short Reports of", ""), 1)
   x2 <- sample(c("Clinical", "Child & Adolescent", "Family", "Current", "Preventive", "Emergency", "Novel", ""), 1)
   x3 <- sample(c("Medicine", "Health Care", "Biomedical Science", "Case Reports", "Nursing Studies", "Surgery", "Medical Case Reports", "Obstetrics", "Gynecology", "Family Medicine", "Primary Care", "Nursing", "Psychiatry", "Pulmonology", "Images", "Biology", "Behavioral Science", "Public Health", "Nursing Practice", "Respiratory Care", "Cancer", "Neurology", "Musculoskeletal Disorders", "Epidemiology", "Cardiology", "Vascular Medicine", "Hematology", "Imaging", "Microbiology", "Infectious Diseases", "Oncology", "Neuroinflammation", "Neurodegenerative Diseases", "Gerontology", "Addiction", "Rehabilitation Medicine", "Translational Medicine", "Images

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############################################################################

library(metafor)
library(boot)

dat <- dat.crede2010
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, subset=criterion=="grade")

############################################################################

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rm(list=ls())

setwd("/home/wviechtb/misc/wallpapers/bivariate_normal")

library(mvtnorm)
library(ellipse)

### specify the correlation for the bivariate normal distribution
rho <- 0.4

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# means, SDs, and group sizes
m1 <- 52
m2 <- 40
sd1 <- 6
sd2 <- 4
n1 <- 20
n2 <- 40

# pooled SD
sdp <- ((n1-1)*sd1^2 + (n2-1)*sd2^2) / (n1 + n2 - 2)

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# inspired by: https://github.com/mjskay/uncertainty-examples/blob/master/diamond_plot.md
# to see if we can abuse metafor::forest() and metafor::addpoly() to create such a plot

library(palmerpenguins)
library(metafor)

set.seed(1234)

dat <- penguins
dat <- dat[!is.na(dat$sex),]

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############################################################################

# During the course, we will make use of a number of add-on packages for R.
# Ideally, you should install these packages before the course (then you don't
# have to bother with installing packages during the course). The code below
# can be used to install each of the packages one by one. So, just run each
# command below (e.g., by copy-pasting it into R) line by line.

# If you are asked to create a 'personal library', choose 'yes'. If you are
# asked to choose a 'CRAN mirror', just select the default one (which is

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############################################################################

library(metafor)
library(ape)

############################################################################

# read the documentation for this dataset
help(dat.moura2021)