The bayesplot MCMC module provides various plotting functions for
creating graphical displays of Markov chain Monte Carlo (MCMC) simulations.
The **MCMC plotting functions** section, below, provides links to the
documentation for various categories of MCMC plots. Currently the MCMC
plotting functions accept posterior draws provided in one of the following
formats:

**3-D array**: An`array`

with dimensions`[Iteration, Chain, Parameter]`

in that order.**list**: A`list`

of matrices, where each matrix corresponds to a Markov chain. All of the matrices should have the same number of iterations (rows) and parameters (columns), and parameters should have the same names and be in the same order.**matrix**: A`matrix`

with one column per parameter. If using matrix there should only be a single Markov chain or all chains should already be merged (stacked).**data frame**: There are two types of data frames allowed. Either a data frame with one column per parameter (if only a single chain or all chains have already been merged), or a data frame with one column per parameter plus an additional column`"Chain"`

that contains the chain number (an integer) corresponding to each row in the data frame.

**Note**: typically the user should *not* include warmup iterations
in the object passed to bayesplot plotting functions, although for
certain plots (e.g. trace plots) it can occasionally be useful to include the
warmup iterations for diagnostic purposes.

**Posterior distributions**Histograms and kernel density plots of parameter draws, optionally showing each Markov chain separately.

**Uncertainty intervals**Uncertainty intervals computed from parameter draws.

**Trace plots**Times series of parameter draws.

**Scatterplots**Scatterplots, heatmaps, and pairs plots of parameter draws.

**Combinations**Combination plots (e.g. trace plot + histogram).

**General MCMC diagnostics**MCMC diagnostic plots including Rhat, effective sample size, autocorrelation.

**NUTS diagnostics**Special diagnostic plots for the No-U-Turn Sampler.

**Comparisons to "true" values**Plots comparing MCMC estimates to "true" parameter values (e.g., values used to simulate data).

Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., Gelman, A. (2017). Visualization in Bayesian workflow. arXiv preprint arvix:1709.01449.

Other MCMC: `MCMC-combos`

,
`MCMC-diagnostics`

,
`MCMC-distributions`

,
`MCMC-intervals`

, `MCMC-nuts`

,
`MCMC-parcoord`

, `MCMC-recover`

,
`MCMC-scatterplots`

,
`MCMC-traces`