Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS
Summary statistics and plot methods for runjags class objects
Obtain Input from User With Error Handling
Run or extend a user-specified Bayesian MCMC model in JAGS with automa...
Combining and dividing runjags and MCMC objects
Conversion Between a Named List and a Character String in the R Dump F...
Extract peripheral information from runjags objects
Attempt to Locate a JAGS Install
Load the internal JAGS module provided by runjags
Mutate functions to be used with runjags summary methods
Create a Unique Filename
Extract Any Models, Data, Monitored Variables or Initial Values As Cha...
Importing of saved JAGS simulations with partial error recovery
Run or extend a user-specified Bayesian MCMC model in JAGS from within...
Drop-k and simulated dataset studies using JAGS
The runjags class and available S3 methods
runjags: Interface Utilities, Model Templates, Parallel Computing Meth...
Options for the runjags package
Print methods for runjags helper classes
Generate a generalised linear mixed model (GLMM) specification in JAGS
Create a Hui-Walter model based on paired test data for an arbitrary n...
Analyse the System to Check That JAGS Is Installed
Calculate the Elapsed Time in Sensible Units
Write a complete JAGS model to a text file
User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.