ac                      Calculate the autocorrelation of a single
                        chain, for a specified amount of lags
binary                  Simulated data for a binary logistic regression
                        and its MCMC samples
calc_bin                Calculate binwidths by parameter, based on the
                        total number of bins.
ci                      Calculate Credible Intervals (wide and narrow).
custom.sort             Auxiliary function that sorts Parameter names
                        taking into account numeric values
get_family              Subset a ggs object to get only the parameters
                        with a given regular expression.
ggmcmc                  Wrapper function that creates a single pdf file
                        with all plots that ggmcmc can produce.
ggs                     Import MCMC samples into a ggs object than can
                        be used by all ggs_* graphical functions.
ggs_Rhat                Dotplot of Potential Scale Reduction Factor
                        (Rhat)
ggs_autocorrelation     Plot an autocorrelation matrix
ggs_caterpillar         Caterpillar plot with thick and thin CI
ggs_chain               Auxiliary function that extracts information
                        from a single chain.
ggs_compare_partial     Density plots comparing the distribution of the
                        whole chain with only its last part.
ggs_crosscorrelation    Plot the Cross-correlation between-chains
ggs_density             Density plots of the chains
ggs_diagnostics         Formal diagnostics of convergence and sampling
                        quality
ggs_effective           Dotplot of the effective number of independent
                        draws
ggs_geweke              Dotplot of the Geweke diagnostic, the standard
                        Z-score
ggs_grb                 Gelman-Rubin-Brooks plot (Rhat shrinkage)
ggs_histogram           Histograms of the paramters.
ggs_pairs               Create a plot matrix of posterior simulations
ggs_pcp                 Plot for model fit of binary response
                        variables: percent correctly predicted
ggs_ppmean              Posterior predictive plot comparing the outcome
                        mean vs the distribution of the predicted
                        posterior means.
ggs_ppsd                Posterior predictive plot comparing the outcome
                        standard deviation vs the distribution of the
                        predicted posterior standard deviations.
ggs_rocplot             Receiver-Operator Characteristic (ROC) plot for
                        models with binary outcomes
ggs_running             Running means of the chains
ggs_separation          Separation plot for models with binary response
                        variables
ggs_traceplot           Traceplot of the chains
gl_unq                  Generate a factor with unequal number of
                        repetitions.
linear                  Simulated data for a continuous linear
                        regression and its MCMC samples
plab                    Generate a data frame suitable for matching
                        parameter names with their labels
radon                   Simulations of the parameters of a hierarchical
                        model
roc_calc                Calculate the ROC curve for a set of observed
                        outcomes and predicted probabilities
s                       Simulations of the parameters of a simple
                        linear regression with fake data.
s.binary                Simulations of the parameters of a simple
                        linear regression with fake data.
s.y.rep                 Simulations of the posterior predictive
                        distribution of a simple linear regression with
                        fake data.
sde0f                   Spectral Density Estimate at Zero Frequency.
y                       Values for the observed outcome of a simple
                        linear regression with fake data.
y.binary                Values for the observed outcome of a binary
                        logistic regression with fake data.
