Bayesian Graphical Models using MCMC

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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