Selects the best model by log-likelihood, AIC, or BIC.

## Usage

```
model_select(
x,
models = univariateML_models,
criterion = c("aic", "bic", "loglik"),
na.rm = FALSE,
...
)
```

## Arguments

- x
a (non-empty) numeric vector of data values.

- models
a character vector containing the distribution models to select from; see

`print(univariateML_models)`

.- criterion
the model selection criterion. Must be one of

`"aic"`

,`"bic"`

, and`"loglik"`

.- na.rm
logical. Should missing values be removed?

- ...
unused.

## Value

`model_select`

returns an object of class`univariateML`

. This is a named numeric vector with maximum likelihood
estimates for the parameters of the best fitting model and the following
attributes:

`model`

The name of the model.

`density`

The density associated with the estimates.

`logLik`

The loglikelihood at the maximum.

`support`

The support of the density.

`n`

The number of observations.

`call`

The call as captured my

`match.call`