The function `mcmc`

is used to create a Markov Chain Monte Carlo
object. The input data are taken to be a vector, or a matrix with
one column per variable.

If the optional arguments `start`

, `end`

, and `thin`

are omitted then the chain is assumed to start with iteration 1 and
have thinning interval 1. If `data`

represents a chain that
starts at a later iteration, the first iteration in the chain should
be given as the `start`

argument. Likewise, if `data`

represents a chain that has already been thinned, the thinning
interval should be given as the `thin`

argument.

An mcmc object may be summarized by the `summary`

function
and visualized with the `plot`

function.

MCMC objects resemble time series (`ts`

) objects and have
methods for the generic functions `time`

, `start`

,
`end`

, `frequency`

and `window`

.

```
mcmc(data= NA, start = 1, end = numeric(0), thin = 1)
as.mcmc(x, …)
is.mcmc(x)
```

data

a vector or matrix of MCMC output

start

the iteration number of the first observation

end

the iteration number of the last observation

thin

the thinning interval between consecutive observations

x

An object that may be coerced to an mcmc object

…

Further arguments to be passed to specific methods

`mcmc.list`

,
`mcmcUpgrade`

,
`thin`

,
`window.mcmc`

,
`summary.mcmc`

,
`plot.mcmc`

.