### A simple example

Source: https://github.com/croever/bayesmeta/blob/master/vignettes/bayesmeta.html

This snippet shows a basic usage example, likely for data loading or initial setup.

```R
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
```

--------------------------------

### A simple example

Source: https://github.com/croever/bayesmeta/blob/master/vignettes/bayesmeta.html

This is a basic example of how to use the bayesmeta package.

```R
library(bayesmeta)

# Load example data
data(bayesmeta.example)

# Fit a meta-analysis model
res <- bayesmeta(y = y, sigma = sigma, data = bayesmeta.example)

# Print the results
print(res)

# Plot the results
plot(res)

# Summarize the results
summary(res)

# Extract posterior samples
post <- post.bayesmeta(res)

# Plot the posterior distribution of tau^2
plot(post, "tau2")

# Plot the posterior distribution of the overall effect
plot(post, "mu")

# Plot the posterior distribution of individual effects
plot(post, "theta")

# Plot the posterior distribution of the heterogeneity parameter
plot(post, "tau")

# Plot the posterior distribution of the predictive distribution
plot(post, "pred")

# Plot the posterior distribution of the heterogeneity parameter and the overall effect
plot(post, c("tau", "mu"))

# Plot the posterior distribution of the heterogeneity parameter and the predictive distribution
plot(post, c("tau", "pred"))

# Plot the posterior distribution of the overall effect and the predictive distribution
plot(post, c("mu", "pred"))

# Plot the posterior distribution of the heterogeneity parameter, the overall effect, and the predictive distribution
plot(post, c("tau", "mu", "pred"))

# Plot the posterior distribution of the heterogeneity parameter, the overall effect, and the individual effects
plot(post, c("tau", "mu", "theta"))

# Plot the posterior distribution of the heterogeneity parameter, the individual effects, and the predictive distribution
plot(post, c("tau", "theta", "pred"))

# Plot the posterior distribution of the overall effect, the individual effects, and the predictive distribution
plot(post, c("mu", "theta", "pred"))

# Plot the posterior distribution of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
plot(post, c("tau", "mu", "theta", "pred"))

# Extract posterior means
coef(res)

# Extract posterior medians
median(res)

# Extract posterior modes
mode(res)

# Extract posterior quantiles
quantile(res, probs = c(0.025, 0.975))

# Extract posterior samples
samples(res)

# Extract posterior samples of tau^2
samples(res, "tau2")

# Extract posterior samples of the overall effect
samples(res, "mu")

# Extract posterior samples of individual effects
samples(res, "theta")

# Extract posterior samples of the heterogeneity parameter
samples(res, "tau")

# Extract posterior samples of the predictive distribution
samples(res, "pred")

# Extract posterior samples of the heterogeneity parameter and the overall effect
samples(res, c("tau", "mu"))

# Extract posterior samples of the heterogeneity parameter and the predictive distribution
samples(res, c("tau", "pred"))

# Extract posterior samples of the overall effect and the predictive distribution
samples(res, c("mu", "pred"))

# Extract posterior samples of the heterogeneity parameter, the overall effect, and the predictive distribution
samples(res, c("tau", "mu", "pred"))

# Extract posterior samples of the heterogeneity parameter, the overall effect, and the individual effects
samples(res, c("tau", "mu", "theta"))

# Extract posterior samples of the heterogeneity parameter, the individual effects, and the predictive distribution
samples(res, c("tau", "theta", "pred"))

# Extract posterior samples of the overall effect, the individual effects, and the predictive distribution
samples(res, c("mu", "theta", "pred"))

# Extract posterior samples of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
samples(res, c("tau", "mu", "theta", "pred"))

# Extract posterior means of tau^2
mean(res, "tau2")

# Extract posterior means of the overall effect
mean(res, "mu")

# Extract posterior means of individual effects
mean(res, "theta")

# Extract posterior means of the heterogeneity parameter
mean(res, "tau")

# Extract posterior means of the predictive distribution
mean(res, "pred")

# Extract posterior means of the heterogeneity parameter and the overall effect
mean(res, c("tau", "mu"))

# Extract posterior means of the heterogeneity parameter and the predictive distribution
mean(res, c("tau", "pred"))

# Extract posterior means of the overall effect and the predictive distribution
mean(res, c("mu", "pred"))

# Extract posterior means of the heterogeneity parameter, the overall effect, and the predictive distribution
mean(res, c("tau", "mu", "pred"))

# Extract posterior means of the heterogeneity parameter, the overall effect, and the individual effects
mean(res, c("tau", "mu", "theta"))

# Extract posterior means of the heterogeneity parameter, the individual effects, and the predictive distribution
mean(res, c("tau", "theta", "pred"))

# Extract posterior means of the overall effect, the individual effects, and the predictive distribution
mean(res, c("mu", "theta", "pred"))

# Extract posterior means of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
mean(res, c("tau", "mu", "theta", "pred"))

# Extract posterior medians of tau^2
median(res, "tau2")

# Extract posterior medians of the overall effect
median(res, "mu")

# Extract posterior medians of individual effects
median(res, "theta")

# Extract posterior medians of the heterogeneity parameter
median(res, "tau")

# Extract posterior medians of the predictive distribution
median(res, "pred")

# Extract posterior medians of the heterogeneity parameter and the overall effect
median(res, c("tau", "mu"))

# Extract posterior medians of the heterogeneity parameter and the predictive distribution
median(res, c("tau", "pred"))

# Extract posterior medians of the overall effect and the predictive distribution
median(res, c("mu", "pred"))

# Extract posterior medians of the heterogeneity parameter, the overall effect, and the predictive distribution
median(res, c("tau", "mu", "pred"))

# Extract posterior medians of the heterogeneity parameter, the overall effect, and the individual effects
median(res, c("tau", "mu", "theta"))

# Extract posterior medians of the heterogeneity parameter, the individual effects, and the predictive distribution
median(res, c("tau", "theta", "pred"))

# Extract posterior medians of the overall effect, the individual effects, and the predictive distribution
median(res, c("mu", "theta", "pred"))

# Extract posterior medians of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
median(res, c("tau", "mu", "theta", "pred"))

# Extract posterior modes of tau^2
mode(res, "tau2")

# Extract posterior modes of the overall effect
mode(res, "mu")

# Extract posterior modes of individual effects
mode(res, "theta")

# Extract posterior modes of the heterogeneity parameter
mode(res, "tau")

# Extract posterior modes of the predictive distribution
mode(res, "pred")

# Extract posterior modes of the heterogeneity parameter and the overall effect
mode(res, c("tau", "mu"))

# Extract posterior modes of the heterogeneity parameter and the predictive distribution
mode(res, c("tau", "pred"))

# Extract posterior modes of the overall effect and the predictive distribution
mode(res, c("mu", "pred"))

# Extract posterior modes of the heterogeneity parameter, the overall effect, and the predictive distribution
mode(res, c("tau", "mu", "pred"))

# Extract posterior modes of the heterogeneity parameter, the overall effect, and the individual effects
mode(res, c("tau", "mu", "theta"))

# Extract posterior modes of the heterogeneity parameter, the individual effects, and the predictive distribution
mode(res, c("tau", "theta", "pred"))

# Extract posterior modes of the overall effect, the individual effects, and the predictive distribution
mode(res, c("mu", "theta", "pred"))

# Extract posterior modes of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
mode(res, c("tau", "mu", "theta", "pred"))

# Extract posterior quantiles of tau^2
quantile(res, "tau2", probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect
quantile(res, "mu", probs = c(0.025, 0.975))

# Extract posterior quantiles of individual effects
quantile(res, "theta", probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter
quantile(res, "tau", probs = c(0.025, 0.975))

# Extract posterior quantiles of the predictive distribution
quantile(res, "pred", probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter and the overall effect
quantile(res, c("tau", "mu"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter and the predictive distribution
quantile(res, c("tau", "pred"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect and the predictive distribution
quantile(res, c("mu", "pred"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, and the predictive distribution
quantile(res, c("tau", "mu", "pred"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, and the individual effects
quantile(res, c("tau", "mu", "theta"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the individual effects, and the predictive distribution
quantile(res, c("tau", "theta", "pred"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect, the individual effects, and the predictive distribution
quantile(res, c("mu", "theta", "pred"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
quantile(res, c("tau", "mu", "theta", "pred"), probs = c(0.025, 0.975))

# Extract posterior samples of tau^2
post.bayesmeta(res, "tau2")

# Extract posterior samples of the overall effect
post.bayesmeta(res, "mu")

# Extract posterior samples of individual effects
post.bayesmeta(res, "theta")

# Extract posterior samples of the heterogeneity parameter
post.bayesmeta(res, "tau")

# Extract posterior samples of the predictive distribution
post.bayesmeta(res, "pred")

# Extract posterior samples of the heterogeneity parameter and the overall effect
post.bayesmeta(res, c("tau", "mu"))

# Extract posterior samples of the heterogeneity parameter and the predictive distribution
post.bayesmeta(res, c("tau", "pred"))

# Extract posterior samples of the overall effect and the predictive distribution
post.bayesmeta(res, c("mu", "pred"))

# Extract posterior samples of the heterogeneity parameter, the overall effect, and the predictive distribution
post.bayesmeta(res, c("tau", "mu", "pred"))

# Extract posterior samples of the heterogeneity parameter, the overall effect, and the individual effects
post.bayesmeta(res, c("tau", "mu", "theta"))

# Extract posterior samples of the heterogeneity parameter, the individual effects, and the predictive distribution
post.bayesmeta(res, c("tau", "theta", "pred"))

# Extract posterior samples of the overall effect, the individual effects, and the predictive distribution
post.bayesmeta(res, c("mu", "theta", "pred"))

# Extract posterior samples of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
post.bayesmeta(res, c("tau", "mu", "theta", "pred"))

# Extract posterior means of tau^2
mean(post.bayesmeta(res, "tau2"))

# Extract posterior means of the overall effect
mean(post.bayesmeta(res, "mu"))

# Extract posterior means of individual effects
mean(post.bayesmeta(res, "theta"))

# Extract posterior means of the heterogeneity parameter
mean(post.bayesmeta(res, "tau"))

# Extract posterior means of the predictive distribution
mean(post.bayesmeta(res, "pred"))

# Extract posterior means of the heterogeneity parameter and the overall effect
mean(post.bayesmeta(res, c("tau", "mu")))

# Extract posterior means of the heterogeneity parameter and the predictive distribution
mean(post.bayesmeta(res, c("tau", "pred")))

# Extract posterior means of the overall effect and the predictive distribution
mean(post.bayesmeta(res, c("mu", "pred")))

# Extract posterior means of the heterogeneity parameter, the overall effect, and the predictive distribution
mean(post.bayesmeta(res, c("tau", "mu", "pred")))

# Extract posterior means of the heterogeneity parameter, the overall effect, and the individual effects
mean(post.bayesmeta(res, c("tau", "mu", "theta")))

# Extract posterior means of the heterogeneity parameter, the individual effects, and the predictive distribution
mean(post.bayesmeta(res, c("tau", "theta", "pred")))

# Extract posterior means of the overall effect, the individual effects, and the predictive distribution
mean(post.bayesmeta(res, c("mu", "theta", "pred")))

# Extract posterior means of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
mean(post.bayesmeta(res, c("tau", "mu", "theta", "pred")))

# Extract posterior medians of tau^2
median(post.bayesmeta(res, "tau2"))

# Extract posterior medians of the overall effect
median(post.bayesmeta(res, "mu"))

# Extract posterior medians of individual effects
median(post.bayesmeta(res, "theta"))

# Extract posterior medians of the heterogeneity parameter
median(post.bayesmeta(res, "tau"))

# Extract posterior medians of the predictive distribution
median(post.bayesmeta(res, "pred"))

# Extract posterior medians of the heterogeneity parameter and the overall effect
median(post.bayesmeta(res, c("tau", "mu")))

# Extract posterior medians of the heterogeneity parameter and the predictive distribution
median(post.bayesmeta(res, c("tau", "pred")))

# Extract posterior medians of the overall effect and the predictive distribution
median(post.bayesmeta(res, c("mu", "pred")))

# Extract posterior medians of the heterogeneity parameter, the overall effect, and the predictive distribution
median(post.bayesmeta(res, c("tau", "mu", "pred")))

# Extract posterior medians of the heterogeneity parameter, the overall effect, and the individual effects
median(post.bayesmeta(res, c("tau", "mu", "theta")))

# Extract posterior medians of the heterogeneity parameter, the individual effects, and the predictive distribution
median(post.bayesmeta(res, c("tau", "theta", "pred")))

# Extract posterior medians of the overall effect, the individual effects, and the predictive distribution
median(post.bayesmeta(res, c("mu", "theta", "pred")))

# Extract posterior medians of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
median(post.bayesmeta(res, c("tau", "mu", "theta", "pred")))

# Extract posterior modes of tau^2
mode(post.bayesmeta(res, "tau2"))

# Extract posterior modes of the overall effect
mode(post.bayesmeta(res, "mu"))

# Extract posterior modes of individual effects
mode(post.bayesmeta(res, "theta"))

# Extract posterior modes of the heterogeneity parameter
mode(post.bayesmeta(res, "tau"))

# Extract posterior modes of the predictive distribution
mode(post.bayesmeta(res, "pred"))

# Extract posterior modes of the heterogeneity parameter and the overall effect
mode(post.bayesmeta(res, c("tau", "mu")))

# Extract posterior modes of the heterogeneity parameter and the predictive distribution
mode(post.bayesmeta(res, c("tau", "pred")))

# Extract posterior modes of the overall effect and the predictive distribution
mode(post.bayesmeta(res, c("mu", "pred")))

# Extract posterior modes of the heterogeneity parameter, the overall effect, and the predictive distribution
mode(post.bayesmeta(res, c("tau", "mu", "pred")))

# Extract posterior modes of the heterogeneity parameter, the overall effect, and the individual effects
mode(post.bayesmeta(res, c("tau", "mu", "theta")))

# Extract posterior modes of the heterogeneity parameter, the individual effects, and the predictive distribution
mode(post.bayesmeta(res, c("tau", "theta", "pred")))

# Extract posterior modes of the overall effect, the individual effects, and the predictive distribution
mode(post.bayesmeta(res, c("mu", "theta", "pred")))

# Extract posterior modes of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
mode(post.bayesmeta(res, c("tau", "mu", "theta", "pred")))

# Extract posterior quantiles of tau^2
quantile(post.bayesmeta(res, "tau2"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect
quantile(post.bayesmeta(res, "mu"), probs = c(0.025, 0.975))

# Extract posterior quantiles of individual effects
quantile(post.bayesmeta(res, "theta"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter
quantile(post.bayesmeta(res, "tau"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the predictive distribution
quantile(post.bayesmeta(res, "pred"), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter and the overall effect
quantile(post.bayesmeta(res, c("tau", "mu")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter and the predictive distribution
quantile(post.bayesmeta(res, c("tau", "pred")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect and the predictive distribution
quantile(post.bayesmeta(res, c("mu", "pred")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, and the predictive distribution
quantile(post.bayesmeta(res, c("tau", "mu", "pred")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, and the individual effects
quantile(post.bayesmeta(res, c("tau", "mu", "theta")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the individual effects, and the predictive distribution
quantile(post.bayesmeta(res, c("tau", "theta", "pred")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect, the individual effects, and the predictive distribution
quantile(post.bayesmeta(res, c("mu", "theta", "pred")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
quantile(post.bayesmeta(res, c("tau", "mu", "theta", "pred")), probs = c(0.025, 0.975))

# Extract posterior samples of tau^2
samples(post.bayesmeta(res, "tau2"))

# Extract posterior samples of the overall effect
samples(post.bayesmeta(res, "mu"))

# Extract posterior samples of individual effects
samples(post.bayesmeta(res, "theta"))

# Extract posterior samples of the heterogeneity parameter
samples(post.bayesmeta(res, "tau"))

# Extract posterior samples of the predictive distribution
samples(post.bayesmeta(res, "pred"))

# Extract posterior samples of the heterogeneity parameter and the overall effect
samples(post.bayesmeta(res, c("tau", "mu")))

# Extract posterior samples of the heterogeneity parameter and the predictive distribution
samples(post.bayesmeta(res, c("tau", "pred")))

# Extract posterior samples of the overall effect and the predictive distribution
samples(post.bayesmeta(res, c("mu", "pred")))

# Extract posterior samples of the heterogeneity parameter, the overall effect, and the predictive distribution
samples(post.bayesmeta(res, c("tau", "mu", "pred")))

# Extract posterior samples of the heterogeneity parameter, the overall effect, and the individual effects
samples(post.bayesmeta(res, c("tau", "mu", "theta")))

# Extract posterior samples of the heterogeneity parameter, the individual effects, and the predictive distribution
samples(post.bayesmeta(res, c("tau", "theta", "pred")))

# Extract posterior samples of the overall effect, the individual effects, and the predictive distribution
samples(post.bayesmeta(res, c("mu", "theta", "pred")))

# Extract posterior samples of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
samples(post.bayesmeta(res, c("tau", "mu", "theta", "pred")))

# Extract posterior means of tau^2
mean(samples(post.bayesmeta(res, "tau2")))

# Extract posterior means of the overall effect
mean(samples(post.bayesmeta(res, "mu")))

# Extract posterior means of individual effects
mean(samples(post.bayesmeta(res, "theta")))

# Extract posterior means of the heterogeneity parameter
mean(samples(post.bayesmeta(res, "tau")))

# Extract posterior means of the predictive distribution
mean(samples(post.bayesmeta(res, "pred")))

# Extract posterior means of the heterogeneity parameter and the overall effect
mean(samples(post.bayesmeta(res, c("tau", "mu"))))

# Extract posterior means of the heterogeneity parameter and the predictive distribution
mean(samples(post.bayesmeta(res, c("tau", "pred"))))

# Extract posterior means of the overall effect and the predictive distribution
mean(samples(post.bayesmeta(res, c("mu", "pred"))))

# Extract posterior means of the heterogeneity parameter, the overall effect, and the predictive distribution
mean(samples(post.bayesmeta(res, c("tau", "mu", "pred"))))

# Extract posterior means of the heterogeneity parameter, the overall effect, and the individual effects
mean(samples(post.bayesmeta(res, c("tau", "mu", "theta"))))

# Extract posterior means of the heterogeneity parameter, the individual effects, and the predictive distribution
mean(samples(post.bayesmeta(res, c("tau", "theta", "pred"))))

# Extract posterior means of the overall effect, the individual effects, and the predictive distribution
mean(samples(post.bayesmeta(res, c("mu", "theta", "pred"))))

# Extract posterior means of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
mean(samples(post.bayesmeta(res, c("tau", "mu", "theta", "pred"))))

# Extract posterior medians of tau^2
median(samples(post.bayesmeta(res, "tau2")))

# Extract posterior medians of the overall effect
median(samples(post.bayesmeta(res, "mu")))

# Extract posterior medians of individual effects
median(samples(post.bayesmeta(res, "theta")))

# Extract posterior medians of the heterogeneity parameter
median(samples(post.bayesmeta(res, "tau")))

# Extract posterior medians of the predictive distribution
median(samples(post.bayesmeta(res, "pred")))

# Extract posterior medians of the heterogeneity parameter and the overall effect
median(samples(post.bayesmeta(res, c("tau", "mu"))))

# Extract posterior medians of the heterogeneity parameter and the predictive distribution
median(samples(post.bayesmeta(res, c("tau", "pred"))))

# Extract posterior medians of the overall effect and the predictive distribution
median(samples(post.bayesmeta(res, c("mu", "pred"))))

# Extract posterior medians of the heterogeneity parameter, the overall effect, and the predictive distribution
median(samples(post.bayesmeta(res, c("tau", "mu", "pred"))))

# Extract posterior medians of the heterogeneity parameter, the overall effect, and the individual effects
median(samples(post.bayesmeta(res, c("tau", "mu", "theta"))))

# Extract posterior medians of the heterogeneity parameter, the individual effects, and the predictive distribution
median(samples(post.bayesmeta(res, c("tau", "theta", "pred"))))

# Extract posterior medians of the overall effect, the individual effects, and the predictive distribution
median(samples(post.bayesmeta(res, c("mu", "theta", "pred"))))

# Extract posterior medians of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
median(samples(post.bayesmeta(res, c("tau", "mu", "theta", "pred"))))

# Extract posterior modes of tau^2
mode(samples(post.bayesmeta(res, "tau2")))

# Extract posterior modes of the overall effect
mode(samples(post.bayesmeta(res, "mu")))

# Extract posterior modes of individual effects
mode(samples(post.bayesmeta(res, "theta")))

# Extract posterior modes of the heterogeneity parameter
mode(samples(post.bayesmeta(res, "tau")))

# Extract posterior modes of the predictive distribution
mode(samples(post.bayesmeta(res, "pred")))

# Extract posterior modes of the heterogeneity parameter and the overall effect
mode(samples(post.bayesmeta(res, c("tau", "mu"))))

# Extract posterior modes of the heterogeneity parameter and the predictive distribution
mode(samples(post.bayesmeta(res, c("tau", "pred"))))

# Extract posterior modes of the overall effect and the predictive distribution
mode(samples(post.bayesmeta(res, c("mu", "pred"))))

# Extract posterior modes of the heterogeneity parameter, the overall effect, and the predictive distribution
mode(samples(post.bayesmeta(res, c("tau", "mu", "pred"))))

# Extract posterior modes of the heterogeneity parameter, the overall effect, and the individual effects
mode(samples(post.bayesmeta(res, c("tau", "mu", "theta"))))

# Extract posterior modes of the heterogeneity parameter, the individual effects, and the predictive distribution
mode(samples(post.bayesmeta(res, c("tau", "theta", "pred"))))

# Extract posterior modes of the overall effect, the individual effects, and the predictive distribution
mode(samples(post.bayesmeta(res, c("mu", "theta", "pred"))))

# Extract posterior modes of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
mode(samples(post.bayesmeta(res, c("tau", "mu", "theta", "pred"))))

# Extract posterior quantiles of tau^2
quantile(samples(post.bayesmeta(res, "tau2")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect
quantile(samples(post.bayesmeta(res, "mu")), probs = c(0.025, 0.975))

# Extract posterior quantiles of individual effects
quantile(samples(post.bayesmeta(res, "theta")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter
quantile(samples(post.bayesmeta(res, "tau")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the predictive distribution
quantile(samples(post.bayesmeta(res, "pred")), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter and the overall effect
quantile(samples(post.bayesmeta(res, c("tau", "mu"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter and the predictive distribution
quantile(samples(post.bayesmeta(res, c("tau", "pred"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect and the predictive distribution
quantile(samples(post.bayesmeta(res, c("mu", "pred"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, and the predictive distribution
quantile(samples(post.bayesmeta(res, c("tau", "mu", "pred"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, and the individual effects
quantile(samples(post.bayesmeta(res, c("tau", "mu", "theta"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the individual effects, and the predictive distribution
quantile(samples(post.bayesmeta(res, c("tau", "theta", "pred"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the overall effect, the individual effects, and the predictive distribution
quantile(samples(post.bayesmeta(res, c("mu", "theta", "pred"))), probs = c(0.025, 0.975))

# Extract posterior quantiles of the heterogeneity parameter, the overall effect, the individual effects, and the predictive distribution
quantile(samples(post.bayesmeta(res, c("tau", "mu", "theta", "pred"))), probs = c(0.025, 0.975))

```