Posterior samples using a simple Metropolis Hastings sampler

## Usage

```
gaussian_draws(model, ...)
# S3 method for gam
gaussian_draws(
model,
n,
n_cores = 1L,
index = NULL,
frequentist = FALSE,
unconditional = FALSE,
mvn_method = "mvnfast",
...
)
# S3 method for scam
gaussian_draws(
model,
n,
n_cores = 1L,
index = NULL,
frequentist = FALSE,
parametrized = TRUE,
mvn_method = "mvnfast",
...
)
```

## Arguments

- model
a fitted R model. Currently only models fitted by

`mgcv::gam()`

or`mgcv::bam()`

, or return an object that*inherits*from such objects are supported. Here, "inherits" is used in a loose fashion; models fitted by`scam::scam()`

are support even though those models don't strictly inherit from class`"gam"`

as far as`inherits()`

is concerned.- ...
arguments passed to methods.

- n
numeric; the number of posterior draws to take.

- n_cores
integer; number of CPU cores to use when generating multivariate normal distributed random values. Only used if

`mvn_method = "mvnfast"`

**and**`method = "gaussian"`

.- index
numeric; vector of indices of coefficients to use. Can be used to subset the mean vector and covariance matrix extracted from

`model`

.- frequentist
logical; if

`TRUE`

, the frequentist covariance matrix of the parameter estimates is used. If`FALSE`

, the Bayesian posterior covariance matrix of the parameters is used. See`mgcv::vcov.gam()`

.- unconditional
logical; if

`TRUE`

the Bayesian smoothing parameter uncertainty corrected covariance matrix is used,*if available*for`model`

. See`mgcv::vcov.gam()`

.- mvn_method
character; one of

`"mvnfast"`

or`"mgcv"`

. The default is uses`mvnfast::rmvn()`

, which can be considerably faster at generate large numbers of MVN random values than`mgcv::rmvn()`

, but which might not work for some marginal fits, such as those where the covariance matrix is close to singular.- parametrized
logical; use parametrized coefficients and covariance matrix, which respect the linear inequality constraints of the model. Only for

`scam::scam()`

model fits.