All the data and functions used to produce the book. We do not expect
    most people to use the package for any other reason than to get simple access to the
    'JAGS' model files, the data, and perhaps run some of the simple examples.
    The authors of the book are David Lucy (now sadly deceased) and James Curran. It is 
    anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with
    bibliographic details to follow at that point. Until such time, further information
    can be obtained by emailing James Curran.

Tools are provided to streamline Bayesian analyses in 'JAGS' using 
    the 'jagsUI' package.  Included are functions for extracting output in 
    simpler format, functions for streamlining assessment of convergence, and 
    functions for producing summary plots of output.  Also included is a 
    function that provides a simple template for running 'JAGS' from 'R'.
    Referenced materials can be found at <DOI:10.1214/ss/1177011136>.

Bayesian data analysis usually incurs long runtimes
  and cumbersome custom code.
  A pipeline toolkit tailored to Bayesian statisticians,
  the 'jagstargets' R package is leverages
  'targets' and 'R2jags' to ease this burden.
  'jagstargets' makes it super easy to set up scalable
  JAGS pipelines that automatically parallelize the computation
  and skip expensive steps when the results are already up to date.
  Minimal custom code is required, and there is no need to manually
  configure branching, so usage is much easier than 'targets' alone.
  For the underlying methodology, please refer
  to the documentation of 'targets' <doi:10.21105/joss.02959> and 'JAGS'
  (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.

A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.