
| acf | autocorrelation function for time series |
| as.mcmc(x) | Markov Chain Monte Carlo Objects |
| as.ts.mcmc | Coerce mcmc object to time series |
| autocorr | Autocorrelation function for Markov chains |
| autocorr.plot | Plot autocorrelations for Markov Chains |
| codamenu | Main menu driver for the coda package |
| crosscorr | Cross correlations for MCMC output |
| crosscorr.plot | Plot image of correlation matrix |
| densplot | Probability density function estimate from MCMC output |
| end.mcmc(x) | Time series methods for mcmc objects |
| frequency.mcmc(x) | Time series methods for mcmc objects |
| gelman.diag | Gelman and Rubin's diagnostic |
| geweke.diag | Geweke's convergence diagnostic for Markov chains |
| geweke.plot | Plot of Geweke's convergence diagnostic for Markov chains |
| heidel.diag | Heidelberger and Welch's convergence diagnosics |
| is.mcmc(x) | Markov Chain Monte Carlo Objects |
| join.mcmc | Join replicates of MCMC output |
| mcmc | Markov Chain Monte Carlo Objects |
| multi.menu | Choose multiple options from a menu |
| raftery.diag | Raftery Lewis diagnostic: Calculate the number of iterations required for an MCMC run |
| read.and.check | Read data interactively and check that it satisfies conditions |
| read.bugs | Read BUGS output files |
| read.bugs.interactive | Read BUGS output files interactively |
| spec.pgram | Estimate spectral density from smoothed periodogram |
| start.mcmc(x) | Time series methods for mcmc objects |
| summary(mcmc.obj, | |
| thin | thin |
| thin.mcmc(x) | Time series methods for mcmc objects |
| time.mcmc | Time series methods for mcmc objects |
| window.mcmc | Time windows for mcmc objects |