Function reference • bayesplot

Overview Package overview
`bayesplot-package` `bayesplot`	bayesplot: Plotting for Bayesian Models
Aesthetics Functions for setting the color scheme and ggplot theme used by bayesplot. (Also see the separate ggplot helpers section below.)
`color_scheme_set()` `color_scheme_get()` `color_scheme_view()`	Set, get, or view bayesplot color schemes
`bayesplot_theme_get()` `bayesplot_theme_set()` `bayesplot_theme_update()` `bayesplot_theme_replace()`	Get, set, and modify the active bayesplot theme
`theme_default()`	Default bayesplot plotting theme
PPC Functions for carrying out a wide variety of graphical model checks based on comparing observed data to draws from the posterior or prior predictive distribution.
`PPC-overview` `PPC`	Graphical posterior predictive checking
`ppc_km_overlay()` `ppc_km_overlay_grouped()`	PPC censoring
`ppc_bars()` `ppc_bars_grouped()` `ppc_rootogram()` `ppc_bars_data()`	PPCs for discrete outcomes
`ppc_data()` `ppc_dens_overlay()` `ppc_dens_overlay_grouped()` `ppc_ecdf_overlay()` `ppc_ecdf_overlay_grouped()` `ppc_dens()` `ppc_hist()` `ppc_freqpoly()` `ppc_freqpoly_grouped()` `ppc_boxplot()` `ppc_violin_grouped()` `ppc_pit_ecdf()` `ppc_pit_ecdf_grouped()`	PPC distributions
`ppc_error_hist()` `ppc_error_hist_grouped()` `ppc_error_scatter()` `ppc_error_scatter_avg()` `ppc_error_scatter_avg_grouped()` `ppc_error_scatter_avg_vs_x()` `ppc_error_binned()` `ppc_error_data()`	PPC errors
`ppc_intervals()` `ppc_intervals_grouped()` `ppc_ribbon()` `ppc_ribbon_grouped()` `ppc_intervals_data()` `ppc_ribbon_data()`	PPC intervals
`ppc_loo_pit_overlay()` `ppc_loo_pit_data()` `ppc_loo_pit_qq()` `ppc_loo_pit()` `ppc_loo_intervals()` `ppc_loo_ribbon()`	LOO predictive checks
`ppc_scatter()` `ppc_scatter_avg()` `ppc_scatter_avg_grouped()` `ppc_scatter_data()` `ppc_scatter_avg_data()`	PPC scatterplots
`ppc_stat()` `ppc_stat_grouped()` `ppc_stat_freqpoly()` `ppc_stat_freqpoly_grouped()` `ppc_stat_2d()` `ppc_stat_data()`	PPC test statistics
`pp_check()`	Posterior (or prior) predictive checks (S3 generic and default method)
PPD Functions for creating graphical displays of simulated data from the posterior or prior predictive distribution (PPD). These plots are essentially the same as the corresponding PPC plots but without comparing to any observed data.
`PPD-overview` `PPD`	Plots of posterior or prior predictive distributions
`ppd_data()` `ppd_dens_overlay()` `ppd_ecdf_overlay()` `ppd_dens()` `ppd_hist()` `ppd_freqpoly()` `ppd_freqpoly_grouped()` `ppd_boxplot()`	PPD distributions
`ppd_intervals()` `ppd_intervals_grouped()` `ppd_ribbon()` `ppd_ribbon_grouped()` `ppd_intervals_data()` `ppd_ribbon_data()`	PPD intervals
`ppd_stat()` `ppd_stat_grouped()` `ppd_stat_freqpoly()` `ppd_stat_freqpoly_grouped()` `ppd_stat_2d()` `ppd_stat_data()`	PPD test statistics
MCMC Functions for creating plots of MCMC draws of model parameters and general MCMC diagnostics.
`MCMC-overview` `MCMC`	Plots for Markov chain Monte Carlo simulations
`mcmc_rhat()` `mcmc_rhat_hist()` `mcmc_rhat_data()` `mcmc_neff()` `mcmc_neff_hist()` `mcmc_neff_data()` `mcmc_acf()` `mcmc_acf_bar()`	General MCMC diagnostics
`mcmc_hist()` `mcmc_dens()` `mcmc_hist_by_chain()` `mcmc_dens_overlay()` `mcmc_dens_chains()` `mcmc_dens_chains_data()` `mcmc_violin()`	Histograms and kernel density plots of MCMC draws
`mcmc_intervals()` `mcmc_areas()` `mcmc_areas_ridges()` `mcmc_intervals_data()` `mcmc_areas_data()` `mcmc_areas_ridges_data()`	Plot interval estimates from MCMC draws
`mcmc_recover_intervals()` `mcmc_recover_scatter()` `mcmc_recover_hist()`	Compare MCMC estimates to "true" parameter values
`mcmc_scatter()` `mcmc_hex()` `mcmc_pairs()` `scatter_style_np()` `pairs_style_np()` `pairs_condition()`	Scatterplots of MCMC draws
`mcmc_parcoord()` `mcmc_parcoord_data()` `parcoord_style_np()`	Parallel coordinates plot of MCMC draws
`mcmc_trace()` `mcmc_trace_highlight()` `trace_style_np()` `mcmc_rank_overlay()` `mcmc_rank_hist()` `mcmc_rank_ecdf()` `mcmc_trace_data()`	Trace and rank plots of MCMC draws
`mcmc_combo()`	Combination plots
HMC/NUTS diagnostics Functions for plotting diagnostics specific to Hamiltonian Monte Carlo (HMC) and the No-U-Turn Sampler (NUTS). Some of the general MCMC plotting functions (`mcmc_parcoord()`, `mcmc_pairs()`, `mcmc_scatter()`, `mcmc_trace()`) can also show HMC/NUTS diagnostic information if optional arguments are specified, but the special functions below are only intended for use with HMC/NUTS.
`mcmc_nuts_acceptance()` `mcmc_nuts_divergence()` `mcmc_nuts_stepsize()` `mcmc_nuts_treedepth()` `mcmc_nuts_energy()`	Diagnostic plots for the No-U-Turn-Sampler (NUTS)
Tidy parameter selection for MCMC plots Helper functions for tidy parameter selection and examples of using bayesplot with dplyr.
`param_range()` `param_glue()`	Tidy parameter selection
ggplot helpers Convenience functions for arranging multiple plots, adding features to plots, and shortcuts for modifying individual ggplot theme elements.
`bayesplot_grid()`	Arrange plots in a grid
`vline_at()` `hline_at()` `vline_0()` `hline_0()` `abline_01()` `lbub()` `legend_move()` `legend_none()` `legend_text()` `xaxis_title()` `xaxis_text()` `xaxis_ticks()` `yaxis_title()` `yaxis_text()` `yaxis_ticks()` `facet_text()` `facet_bg()` `panel_bg()` `plot_bg()` `grid_lines()` `overlay_function()`	Convenience functions for adding or changing plot details
Extractors Functions extracting various quantities needed for plotting from different types of fitted model objects.
`log_posterior()` `nuts_params()` `rhat()` `neff_ratio()`	Extract quantities needed for plotting from model objects
Miscellaneous Functions for generating data for examples and listing available plotting functions.
`example_mcmc_draws()` `example_yrep_draws()` `example_y_data()` `example_x_data()` `example_group_data()`	Example draws to use in demonstrations and tests
`available_ppc()` `available_ppd()` `available_mcmc()`	Get or view the names of available plotting or data functions

Reference

Overview

Aesthetics

PPC

PPD

MCMC

HMC/NUTS diagnostics

Tidy parameter selection for MCMC plots

ggplot helpers

Extractors

Miscellaneous