**bayesplot** is an R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC). The plots created by **bayesplot** are ggplot objects, which means that after a plot is created it can be further customized using various functions from the **ggplot2** package.

Currently **bayesplot** offers a variety of plots of posterior draws, visual MCMC diagnostics, and graphical posterior (or prior) predictive checking. Additional functionality (e.g. for forecasting/out-of-sample prediction and other inference-related tasks) will be added in future releases.

The idea behind **bayesplot** is not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of packages for Bayesian modeling, particularly (but not necessarily) those powered by **RStan**.

If you are just getting started with **bayesplot** we recommend starting with the tutorial vignettes, the examples throughout the package documentation, and the paper *Visualization in Bayesian workflow*:

- Gabry et al. (2019). Visualization in Bayesian workflow.
*J. R. Stat. Soc. A*, 182: 389-402. doi:10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)

Install the latest release from **CRAN**:

`install.packages("bayesplot")`

Install the latest development version from **GitHub**:

```
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("stan-dev/bayesplot")
```

Installation from GitHub does not include the vignettes by default because they take some time to build, but the vignettes can always be accessed online anytime at mc-stan.org/bayesplot/articles).

Some quick examples using MCMC draws obtained from our **rstanarm** and **rstan** packages.

```
library("bayesplot")
library("rstanarm")
library("ggplot2")
fit <- stan_glm(mpg ~ ., data = mtcars)
posterior <- as.matrix(fit)
plot_title <- ggtitle("Posterior distributions",
"with medians and 80% intervals")
mcmc_areas(posterior,
pars = c("cyl", "drat", "am", "wt"),
prob = 0.8) + plot_title
```

```
color_scheme_set("red")
ppc_dens_overlay(y = fit$y,
yrep = posterior_predict(fit, draws = 50))
```

```
# also works nicely with piping
library("dplyr")
color_scheme_set("brightblue")
fit %>%
posterior_predict(draws = 500) %>%
ppc_stat_grouped(y = mtcars$mpg,
group = mtcars$carb,
stat = "median")
```

```
# with rstan demo model
library("rstan")
fit2 <- stan_demo("eight_schools", warmup = 300, iter = 700)
posterior2 <- extract(fit2, inc_warmup = TRUE, permuted = FALSE)
color_scheme_set("mix-blue-pink")
p <- mcmc_trace(posterior2, pars = c("mu", "tau"), n_warmup = 300,
facet_args = list(nrow = 2, labeller = label_parsed))
p + facet_text(size = 15)
```

```
# scatter plot also showing divergences
color_scheme_set("darkgray")
mcmc_scatter(
as.matrix(fit2),
pars = c("tau", "theta[1]"),
np = nuts_params(fit2),
np_style = scatter_style_np(div_color = "green", div_alpha = 0.8)
)
```

```
color_scheme_set("red")
np <- nuts_params(fit2)
mcmc_nuts_energy(np) + ggtitle("NUTS Energy Diagnostic")
```

```
# another example with rstanarm
color_scheme_set("purple")
fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars)
ppc_intervals(
y = mtcars$mpg,
yrep = posterior_predict(fit),
x = mtcars$wt,
prob = 0.5
) +
labs(
x = "Weight (1000 lbs)",
y = "MPG",
title = "50% posterior predictive intervals \nvs observed miles per gallon",
subtitle = "by vehicle weight"
) +
panel_bg(fill = "gray95", color = NA) +
grid_lines(color = "white")
```