An R package providing a library of plotting functions for use after fitting Bayesian models (typically with MCMC). The idea 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.

The plots created by bayesplot are ggplot objects, which means that after a plot is created it can be further customized using the various functions for modifying ggplot objects provided by the ggplot2 package.

bayesplot is not yet on CRAN (coming soon) but can be installed from GitHub using the devtools package. You will also need the preview version of the upcoming ggplot2 update.

if (!require("devtools"))
  install.packages("devtools")

devtools::install_github("hadley/ggplot2")
devtools::install_github("stan-dev/bayesplot", dependencies = TRUE, build_vignettes = TRUE)

If you are not using RStudio and you get an error related to "pandoc" you will either need to install pandoc or remove the argument build_vignettes=TRUE to avoid building the package vignettes.

Some quick examples using MCMC draws obtained from the 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

<img src=https://github.com/jgabry/bayesplot/blob/master/images/ppc_stat_grouped-rstanarm.png width=50% />
```r
# 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)

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")

<img src=https://github.com/jgabry/bayesplot/blob/master/images/ppc_intervals-rstanarm.png width=55% />