whiskerplot
(Package: jagsUI) :
Whisker plots of parameter posterior distributions
Displays whisker plots for specified parameters on the same plot, with a point at the mean value for the posterior distribution and whiskers extending to the specified quantiles of the distribution.
Displays a series of MCMC iteration plots for each monitored parameter in a JAGS analysis. The calculated Rhat value for each parameter is given in the plot title if there is >1 chains.
jags.basic
(Package: jagsUI) :
Simplified function to call JAGS from R
The jags.basic function is a simplified version of the jags function which returns only the mcmc.list-class output from rjags rather than a more complex summary (it will also optionally return the model, in which case the output object will be class jagsUIbasic). This minimal function may be useful when the input dataset or output parameter set are very large and memory intensive.
autojags
(Package: jagsUI) :
Automatically run jagsUI analyses to convergence
The autojags function runs repeated updates of jagsUI models, until a specified convergence level (based on the statistic Rhat) or a maximum number of iterations is reached.
The jags function is a basic user interface for running JAGS analyses via package rjags inspired by similar packages like R2WinBUGS, R2OpenBUGS, and R2jags. The user provides a model file, data, initial values (optional), and parameters to save. The function compiles the information and sends it to JAGS, then consolidates and summarizes the MCMC output in an object of class jagsUI.
pp.check
(Package: jagsUI) :
Posterior Predictive Checks for Bayesian Analyses fit in JAGS
A simple interface for generating a posterior predictive check plot for a JAGS analysis fit using jagsUI, based on the posterior distributions of discrepency metrics specified by the user and calculated and returned by JAGS (for example, sums of residuals). The user supplies the name of the discrepancy metric calculated for the real data in the argument actual, and the corresponding discrepancy for data simulated by the model in argument new. The posterior distributions of the two parameters will be plotted in X-Y space and a Bayesian p-value calculated.
providing 
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