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 classunivariateML. 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

model_select(precip)