rjags-package {rjags} | R Documentation |

##
Bayesian graphical models using MCMC

### Description

The rjags package provides an interface from R to the JAGS library for
Bayesian data analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to
generate a sequence of dependent samples from the posterior
distribution of the parameters.

### Details

JAGS is a clone of BUGS (Bayesian analysis Using Gibbs Sampling). See
Lunn et al (2009) for a history of the BUGS project. Note that the
rjags package does not include a copy of the JAGS library: you must
install this separately. For instructions on downloading JAGS, see the
home page at https://mcmc-jags.sourceforge.io.

To fully understand how JAGS works, you need to read the
JAGS
User Manual. The manual explains the basics of modelling with JAGS
and shows the functions and distributions available in the dialect of
the BUGS language used by JAGS. It also describes the command line
interface. The rjags package does not use the command line
interface but provides equivalent functionality using R functions.

Analysis using the rjags package proceeds in steps:

Define the model using the BUGS language in a separate file.

Read in the model file using the `jags.model`

function.
This creates an object of class “jags”.

Update the model using the `update`

method for
“jags” objects. This constitutes a ‘burn-in’ period.

Extract samples from the model object using the
`coda.samples`

function. This creates an object of class
“mcmc.list” which can be used to summarize the posterior
distribution. The coda package also provides convergence
diagnostics to check that the output is valid for analysis (see
Plummer et al 2006).

### Author(s)

Martyn Plummer

### References

Lunn D, Spiegelhalter D, Thomas A, Best N. (2009) The BUGS project:
Evolution, critique and future directions. *Statistics in Medicine*,
**28**:3049-67.

Plummer M, Best N, Cowles K, Vines K (2006). CODA: Convergence Diagnosis
and Output Analysis for MCMC, *R News*, **6**:7-11.

[Package

*rjags* version 4-13

Index]