Plotting for Bayesian Models
Get or view the names of available plotting or data functions
Set, get, or view bayesplot color schemes
Extract quantities needed for plotting from model objects
Convenience functions for adding or changing plot details
bayesplot : Plotting for Bayesian Models
Arrange plots in a grid
Get, set, and modify the active bayesplot theme
Example draws to use in demonstrations and tests
Combination plots
General MCMC diagnostics
Histograms and kernel density plots of MCMC draws
Plot interval estimates from MCMC draws
Diagnostic plots for the No-U-Turn-Sampler (NUTS)
Plots for Markov chain Monte Carlo simulations
Parallel coordinates plot of MCMC draws
Compare MCMC estimates to "true" parameter values
Scatterplots of MCMC draws
Trace and rank plots of MCMC draws
Posterior (or prior) predictive checks (S3 generic and default method)
PPC censoring
PPCs for discrete outcomes
PPC distributions
PPC errors
PPC intervals
LOO predictive checks
Graphical posterior predictive checking
PPC scatterplots
PPC test statistics
PPD distributions
PPD intervals
Plots of posterior or prior predictive distributions
PPD test statistics
Objects exported from other packages
Default bayesplot plotting theme
Tidy parameter selection
Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed 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 R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.