Bayesian Graphical Models using MCMC


[Up] [Top]

Documentation for package ‘rjags’ version 4-12

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