[SciPy-Dev] add johnson SL distribution

[josef.pktd at gmail.com]

On Thu, Jan 3, 2019 at 3:54 PM <josef.pktd at gmail.com> wrote:

>
>
> On Thu, Jan 3, 2019 at 3:31 PM Matt Haberland <haberland at ucla.edu> wrote:
>
>> I am not personally familiar with the Johnson family of distributions
>> <https://books.google.com/books?id=_LvgBwAAQBAJ&pg=PA197&lpg=PA197&dq=johns+su+sb+sl+distributions&source=bl&ots=LBowBmYTse&sig=9KPViyvSlLAFp9EYqi-ejTYgQ30&hl=en&sa=X&ved=2ahUKEwjE6cnvt9LfAhWG458KHdrQAmkQ6AEwDXoECAIQAQ#v=onepage&q=johns%20su%20sb%20sl%20distributions&f=false>,
>> but the SL does seem to complete the set.
>>
>> The license for the Matlab implementation does seem to be BSD 3-clause
>> <https://en.wikipedia.org/wiki/BSD_licenses#3-clause> and thus
>> compatible with SciPy.
>>
>> Seems like a reasonable first issue, but certainly finishing stalled PRs
>> would be helpful, too!
>>
>> Matt Haberland
>>
>> On Thu, Jan 3, 2019 at 10:09 AM Michael Watson <
>> mike.watson at sheffield.ac.uk> wrote:
>>
>>> Hi all, happy new year,
>>> We have the SB and SU Johnson distributions implemented but not the SL
>>> distribution, it doesn't look like much work to add it in if it's
>>> appropriate, I'm doing some work with these distributions and ultimately
>>> would like to implement functions to fit by moments and by quantiles too.
>>> there are existing implementations that are distributed under the BSD
>>> licence here:
>>>
>>>
>>> https://uk.mathworks.com/matlabcentral/fileexchange/46123-johnson-curve-toolbox
>>>
>>> so it doesn't seem like a big job from my point of view and I'll be
>>> doing it anyway.
>>>
>>> it would also be my first contribution so if it would be better to start
>>> with another issue (I saw a list and 2 stalled PRs in another email) then
>>> try to add functionality just say and I can look at contributing other ways
>>> first.
>>>
>>
> In general to adding new distributions
>
> The speed of getting a new distribution in depends a lot on how well it
> fits into the general distribution pattern and whether all core methods are
> available as closed form expression or by using scipy.special functions.
> If that is the case, then adding a new distribution is easy.
> If that is not the case, then it can be difficult to get a good version
> merged. One difficult case is if the pdf is only available as
> computationally expensive numerical approximation.
>
> The distributions have in general only the fit method using maximum
> likelihood estimation of parameters (which might reduce to method of
> moments in special cases).
>
> Based on a quick search it looks like JohnsonSL is just the log-normal
> distribution (as loc-scale family which is available in scipy)
>

scipy lognorm is a 3 parameter family, maybe there should also be a 4
parameter family


>
> Josef
>
>
>> Mike
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>>>
>>
>>
>> --
>> Matt Haberland
>> Assistant Adjunct Professor in the Program in Computing
>> Department of Mathematics
>> 6617A Math Sciences Building, UCLA
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>>
>
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