A convenient wrapper function (`posterior`

) to sample the posterior using MCMC in rstan
and returns a `stanfit`

object for diagnostics. Use `RCMstan`

to update the RCM and the enclosed operating model
with MCMC samples..

```
posterior(x, ...)
# S4 method for RCModel
posterior(
x,
priors_only = FALSE,
laplace = FALSE,
chains = 2,
iter = 2000,
warmup = floor(iter/2),
thin = 5,
seed = 34,
init = "last.par.best",
cores = chains,
...
)
# S4 method for Assessment
posterior(x, priors_only = FALSE, ...)
RCMstan(RCModel, stanfit, sim, cores = 1)
```

- x
An object of class Assessment or RCModel.

- ...
Additional arguments to pass to

`rstan::sampling`

via`tmbstan::tmbstan`

.- priors_only
Logical, whether to set the likelihood to zero and sample the priors only.

- laplace
Logical, whether to do the Laplace approximation for random parameters.

- chains
The numer of MCMC chains.

- iter
The number of iterations for each chain, including warmup.

- warmup
The number of burnin iterations

- thin
The frequency at which iterations are kept (e.g.,

`5`

saves every fifth iteration)- seed
Seed for random number generator during the MCMC.

- init
The initial values of parameters for starting the MCMC chain. See

`tmbstan::tmbstan`

.- cores
The number of cores for running in parallel, e.g., one core per MCMC chain. Used in

`RCMstan`

for reconstructing the population.- RCModel
An object of class

`RCModel`

- stanfit
An object of class

`stanfit`

returned by`posterior`

.- sim
A matrix of

`RCModel@OM@nsim`

rows and 2 columns that specifies the samples used to update the operating model. The first column specifies the chain and the second columns specifies the MCMC iteration.

`posterior`

returns an object of class `stanfit`

. See `class?stanfit`

.

`RCMstan`

returns an updated `RCModel`

.

A vignette on the steps to run the MCMC is available on the openMSE website.