Use iterated Laplace approximation as a proposal for importance sampling or the independence Metropolis Hastings algorithm.

`IS(obj, nSim, df = 4, post, vectorized = FALSE, cores = 1, ...)`IMH(obj, nSim, df = 4, post, vectorized = FALSE, cores = 1, ...)

obj

an object of class "mixDist"

nSim

number of simulations

df

degrees of freedom of the mixture of t distributions proposal

post

log-posterior density

vectorized

Logical determining, whether `post`

is vectorized

cores

number of cores you want to use to evaluate the target density (uses the mclapply function from the parallel package). For Windows machines, a value > 1 will have no effect, see mclapply help for details.

…

additional arguments passed to `post`

.

A list with entries:
`samp`

: Matrix containing sampled values
`w`

: Vector of weights for values in samp
`normconst`

: normalization constant estimated based on importance
sampling
`ESS`

: Effective sample size (for IS)
`accept`

: Acceptance rate (for IMH)

# NOT RUN { ## see function iterLap for an example on how to use IS and IMH # }