Uses stat::nlm to estimate the parameters of the Beta distribution.

## Usage

mlbeta(x, na.rm = FALSE, ...)

## 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 classunivariateML. 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.

AIC(mlbeta(USArrests\$Rape / 100))