posterior: Tools for Working with Posterior Distributions in R

posterior: Tools for Working with Posterior Distributions in R - Published in JOSS (2026)

https://github.com/stan-dev/posterior

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The posterior R package

README.Rmd

---
output: github_document
---



```{r, include=FALSE}
stopifnot(require(knitr))
options(width = 90)
knitr::opts_chunk$set(
  collapse = TRUE,
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```

# posterior 


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The **posterior** R package is intended to provide useful tools for both users
and developers of packages for fitting Bayesian models or working with output
from Bayesian models. The primary goals of the package are to:

* Efficiently convert between many different useful formats of
  draws (samples) from posterior or prior distributions.
* Provide consistent methods for operations commonly performed on draws, 
  for example, subsetting, binding, or mutating draws.
* Provide various summaries of draws in convenient formats.
* Provide lightweight implementations of state of the art posterior inference 
  diagnostics.
  
If you are new to **posterior** we recommend starting with these vignettes: 

* [*The posterior R package*](https://mc-stan.org/posterior/articles/posterior.html): 
an introduction to the package and its main functionality 
* [*rvar: The Random Variable Datatype*](https://mc-stan.org/posterior/articles/rvar.html): 
an overview of the new random variable datatype

### Installation

You can install the latest official release version via

```{r install_cran, eval=FALSE}
install.packages("posterior")
```

or build the developmental version directly from GitHub via

```{r install_github, eval=FALSE}
# install.packages("remotes")
remotes::install_github("stan-dev/posterior")
```

### Examples

Here we offer a few examples of using the package. For a more detailed overview
see the vignette [*The posterior R package*](https://mc-stan.org/posterior/articles/posterior.html).

```{r load}
library("posterior")
```

To demonstrate how to work with the **posterior** package, we will use example
posterior draws obtained from the eight schools hierarchical meta-analysis model
described in Gelman et al. (2013). Essentially, we have an estimate per school
(`theta[1]` through `theta[8]`) as well as an overall mean (`mu`) and standard
deviation across schools (`tau`).

#### Draws formats 

```{r draws_array}
eight_schools_array <- example_draws("eight_schools")
print(eight_schools_array, max_variables = 3)
```

The draws for this example come as a `draws_array` object, that is, an array
with dimensions iterations x chains x variables. We can easily transform it to
another format, for instance, a data frame with additional meta information.

```{r draws_df}
eight_schools_df <- as_draws_df(eight_schools_array)
print(eight_schools_df)
```

Different formats are preferable in different situations and hence posterior
supports multiple formats and easy conversion between them. For more details on
the available formats see `help("draws")`. All of the formats are essentially
base R object classes and can be used as such. For example, a `draws_matrix`
object is just a `matrix` with a little more consistency and additional methods.

#### Summarizing draws

Computing summaries of posterior or prior draws and convergence diagnostics for
posterior draws is one of the most common tasks when working with Bayesian
models fit using Markov Chain Monte Carlo (MCMC) methods. The **posterior**
package provides a flexible interface for this purpose via `summarise_draws()`:

```{r summary}
# summarise_draws or summarize_draws
summarise_draws(eight_schools_df)
```

Basically, we get a data frame with one row per variable and one column per
summary statistic or convergence diagnostic. The summaries `rhat`, `ess_bulk`,
and `ess_tail` are described in Vehtari et al. (2020). We can choose which
summaries to compute by passing additional arguments, either functions or names
of functions. For instance, if we only wanted the mean and its corresponding
Monte Carlo Standard Error (MCSE) we would use:

```{r summary-with-measures}
summarise_draws(eight_schools_df, "mean", "mcse_mean")
```

For a function to work with `summarise_draws`, it needs to take a vector or
matrix of numeric values and returns a single numeric value or a named vector of
numeric values.

#### Subsetting draws 

Another common task when working with posterior (or prior) draws, is subsetting
according to various aspects of the draws (iterations, chains, or variables).
**posterior** provides a convenient interface for this purpose via the
`subset_draws()` method. For example, here is the code to extract the first five 
iterations of the first two chains of the variable `mu`:

```{r subset}
subset_draws(eight_schools_df, variable = "mu", chain = 1:2, iteration = 1:5)
```

The same call to `subset_draws()` can be used regardless of whether the object 
is a `draws_df`, `draws_array`, `draws_list`, etc.

#### Mutating and renaming draws

The magic of having obtained draws from the joint posterior (or prior)
distribution of a set of variables is that these draws can also be used
to obtain draws from any other variable that is a function of the original variables.
That is, if are interested in the posterior distribution of, say, 
`phi = (mu + tau)^2` all we have to do is to perform the transformation for each
of the individual draws to obtain draws from the posterior distribution of the
transformed variable. This procedure is automated in the `mutate_variables` method:

```{r}
x <- mutate_variables(eight_schools_df, phi = (mu + tau)^2)
x <- subset_draws(x, c("mu", "tau", "phi"))
print(x)
```

When we do the math ourselves, we see that indeed for each draw, 
`phi` is equal to `(mu + tau)^2` (up to rounding two 2 digits 
for the purpose of printing).

We may also easily rename variables, or even entire vectors of variables via
`rename_variables`, for example:

```{r}
x <- rename_variables(eight_schools_df, mean = mu, alpha = theta)
variables(x)
```

As with all **posterior** methods, `mutate_variables` and `rename_variables` 
can be used with all draws formats.

#### Binding draws together

Suppose we have multiple draws objects that we want to bind together:

```{r}
x1 <- draws_matrix(alpha = rnorm(5), beta = 1)
x2 <- draws_matrix(alpha = rnorm(5), beta = 2)
x3 <- draws_matrix(theta = rexp(5))
```

Then, we can use the `bind_draws` method to bind them along different dimensions.
For example, we can bind `x1` and `x3` together along the `'variable'` dimension:

```{r}
x4 <- bind_draws(x1, x3, along = "variable")
print(x4)
```

Or, we can bind `x1` and `x2` together along the `'draw'` dimension:

```{r}
x5 <- bind_draws(x1, x2, along = "draw")
print(x5)
```

As with all **posterior** methods, `bind_draws` can be used with all draws 
formats.

#### Converting from regular R objects to draws formats

The `eight_schools` example already comes in a format natively supported by
posterior but we could of course also import the draws from other sources,
for example, from common base R objects:

```{r draws_matrix}
x <- matrix(rnorm(50), nrow = 10, ncol = 5)
colnames(x) <- paste0("V", 1:5)
x <- as_draws_matrix(x)
print(x)

summarise_draws(x, "mean", "sd", "median", "mad")
```

Instead of `as_draws_matrix()` we also could have just used `as_draws()`, which
attempts to find the closest available format to the input object. In this case
this would result in a `draws_matrix` object either way.

The above matrix example contained only one chain. Multi-chain draws could be 
stored in base R 3-D array object, which can also be converted to a draws object:

```{r}
x <- array(data = rnorm(200), dim = c(10, 2, 5))
x <- as_draws_matrix(x)
variables(x) <- paste0("V", 1:5)
print(x)
```

#### Converting from mcmc objects to draws formats

The **coda** and **rjags** packages use `mcmc` and `mcmc.list` objects which 
can also be converted to draws objects:

```{r}
data(line, package = "coda")
line <- as_draws_df(line)
print(line)
```

### Contributing to posterior

We welcome contributions!
The **posterior** package is under active development.
If you find bugs or have ideas for new features (for us or yourself to
implement) please [open an issue](https://github.com/stan-dev/posterior/issues) on GitHub.
See [CONTRIBUTING.md](https://github.com/stan-dev/posterior/blob/master/.github/CONTRIBUTING.md)
for more details.

### Citing posterior

Developing and maintaining open source software is an important yet often
underappreciated contribution to scientific progress. Thus, whenever you are
using open source software (or software in general), please make sure to cite it
appropriately so that developers get credit for their work.

When using **posterior**, please cite it as follows:

* Bürkner P. C., Gabry J., Kay M., & Vehtari A. (2020). “posterior: Tools for
Working with Posterior Distributions.” R package version XXX, .
  
When using the MCMC convergence diagnostics `rhat`, `ess_bulk`, `ess_tail`,
`ess_median`, `ess_quantile`, `mcse_median`, or `mcse_quantile`
please also cite

* Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021).
Rank-normalization, folding, and localization: An improved Rhat for assessing
convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667–718.
doi.org/10.1214/20-BA1221

When using the MCMC convergence diagnostic `rhat_nested`
please also cite

* Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L.,
  Vehtari, A., and Gelman, A. (2024).
  Nested $\widehat{R}$: Assessing the convergence of Markov chain
  Monte Carlo when running many short chains. *Bayesian Analysis*,
  doi:10.1214/24-BA1453. 

When using the MCMC convergence diagnostic `rstar`
please also cite

* Lambert, B. and Vehtari, A. (2022). $R^*$: A robust MCMC convergence
  diagnostic with uncertainty using decision tree classifiers.
  *Bayesian Analysis*, 17(2):353-379.
  doi:10.1214/20-BA1252

When using the Pareto-k diagnostics `pareto_khat`, `pareto_min_ss`,
`pareto_convergence_rate`, `khat_threshold` or `pareto_diags`, or
Pareto smoothing `pareto_smooth` please also cite

* Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
Pareto smoothed importance sampling.
*Journal of Machine Learning Research*, 25(72):1-58.

The same information can be obtained by running `citation("posterior")`.

### References

Gelman A., Carlin J. B., Stern H. S., David B. Dunson D. B., Aki Vehtari A.,
& Rubin D. B. (2013). *Bayesian Data Analysis, Third Edition*. Chapman and
Hall/CRC.

Lambert, B. and Vehtari, A. (2022). $R^*$: A robust MCMC convergence
diagnostic with uncertainty using decision tree classifiers.
*Bayesian Analysis*, 17(2):353-379.
doi:10.1214/20-BA1252

Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L.,
Vehtari, A., and Gelman, A. (2024).
Nested $\widehat{R}$: Assessing the convergence of Markov chain
Monte Carlo when running many short chains. *Bayesian Analysis*,
doi:10.1214/24-BA1453.
  
Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021).
Rank-normalization, folding, and localization: An improved Rhat for assessing
convergence of MCMC (with discussion). *Bayesian Analysis*. 16(2), 667–718.
doi.org/10.1214/20-BA1221

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024).
Pareto smoothed importance sampling.
*Journal of Machine Learning Research*, 25(72):1-58.

### Licensing

The **posterior** package is licensed under the following licenses:

- Code: BSD 3-clause (https://opensource.org/license/bsd-3-clause)
- Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Owner

JOSS Publication

posterior: Tools for Working with Posterior Distributions in R

Published

June 25, 2026

DOI

10.21105/joss.10526

Volume 11, Issue 122, Page 10526

Authors

Paul-Christian Bürkner
TU Dortmund University, Germany

Jonah Gabry
Columbia University, USA

Matthew Kay
Northwestern University, USA

Aki Vehtari
Aalto University and ELLIS Institute Finland, Finland

Editor

Adithi R Upadhya

Tags

Bayesian Inference MCMC Diagnostics

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