There is this one:

Although I recall there are some small differences with the journal version.

The sinh-arcsinh transformation is quite interesting, indeed, although the induced distribution cannot capture high levels of skewness (https://arxiv.org/abs/1307.6021).

]]>After writing this package I feel – similarly to Christian – that g&k style distributions aren’t of great practical use. The extra flexibility doesn’t seem enough to make up for the difficulty of fitting them. But I’d be happy to be proved wrong!

However I do like the strategy of creating distributions by transforming random variables. There are some interesting ML papers which do this by composing many transformations (e.g. https://arxiv.org/abs/1605.08803). It would be nice to use transformations from the quantile distributions literature here, particularly to allow light/heavy tails.

]]>Thank you. Do you know of an open access version for this paper?

]]>An interesting, relatively balanced, catalogue of more tractable parametric flexible distributions can be found in the following discussion paper:

http://onlinelibrary.wiley.com/doi/10.1111/insr.12055/abstract

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