Uses `stat::nlm`

to estimate the parameters of the Beta distribution.

## Arguments

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

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

- ...
`start`

contains optional starting parameter values for the minimization, passed to the`stats::nlm`

function.`type`

specifies whether a dedicated`"gradient"`

,`"hessian"`

, or`"none"`

should be passed to`stats::nlm`

.

## Value

`mlbeta`

returns an object of class`univariateML`

. This is a named numeric vector with maximum
likelihood estimates for `shape1`

and `shape2`

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`

## Details

For the density function of the Beta distribution see Beta.

For `type`

, the option `none`

is fastest.

## References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 2, Chapter 25. Wiley, New York.

## Examples

```
AIC(mlbeta(USArrests$Rape / 100))
#> [1] -98.78715
```