The maximum likelihood estimate of `meanlog`

is the empirical mean of the
log-transformed data and the maximum likelihood estimate of `sdlog`

is the square root of the biased sample variance based on the
log-transformed data.

## Arguments

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

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

- ...
currently affects nothing.

## Value

`mllonorm`

returns an object of class`univariateML`

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

and `sdlog`

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 log normal distribution see Lognormal.

## References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 14. Wiley, New York.

## See also

Lognormal for the log normal density.