The ets() function uses maximum likelihood estimation. So it would be possible to obtain standard errors based on the Hessian matrix in the usual way. However, in forecasting, the value of the model parameters is usually of very limited interest -- what we care about are the forecasts and their variances.
I can't think of a situation where you might want a confidence interval for a smoothing parameter, for example. What could you do with the information that the "true" value of alpha (whatever that means) lies between 0.2 and 0.4?
Consequently, I have not included the calculation of the standard errors of parameters in the package.