R: Bayesian Graphical Models using MCMC

# Bayesian Graphical Models using MCMC

## Documentation for package ‘rjags’ version 4-17

- DESCRIPTION file

## Help Pages

rjags-package | Bayesian graphical models using MCMC
adapt | Adaptive phase for JAGS models
as.mcmc.list.mcarray | Objects for representing MCMC output
coda.samples | Generate posterior samples in mcmc.list format
coef.jags | Functions for manipulating jags model objects
dic | Generate penalized deviance samples
dic.samples | Generate penalized deviance samples
diffdic | Differences in penalized deviance
jags.model | Create a JAGS model object
jags.samples | Generate posterior samples
JAGS.version | JAGS version
jags.version | JAGS version
LINE | Linear regression example
list.factories | Advanced control over JAGS
list.modules | Dynamically load JAGS modules
list.samplers | Functions for manipulating jags model objects
load.module | Dynamically load JAGS modules
mcarray.object | Objects for representing MCMC output
parallel.seeds | Get initial values for parallel RNGs
print.mcarray | Objects for representing MCMC output
read.bugsdata | Read data files for jags models
read.data | Deprecated Functions in the rjags package
read.jagsdata | Read data files for jags models
rjags | Bayesian graphical models using MCMC
rjags-deprecated | Deprecated Functions in the rjags package
set.factory | Advanced control over JAGS
summary.mcarray | Objects for representing MCMC output
unload.module | Dynamically load JAGS modules
update.jags | Update jags models
variable.names.jags | Functions for manipulating jags model objects