# Is there a package implementing the Box and Cox (1964) method for transforming data?

**URL:** <https://discourse.julialang.org/t/is-there-a-package-implementing-the-box-and-cox-1964-method-for-transforming-data/14024>\
**Category:** Statistics\
**Tags:** question\
**Created:** [August 25, 2018, 3:26am UTC](https://discourse.julialang.org/t/is-there-a-package-implementing-the-box-and-cox-1964-method-for-transforming-data/14024 "2018-08-25T03:26:48Z")\
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**Author:** ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)\
**Post date:** [August 25, 2018, 5:36am UTC](https://discourse.julialang.org/t/is-there-a-package-implementing-the-box-and-cox-1964-method-for-transforming-data/14024/2 "2018-08-25T05:36:37Z")

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I may be missing something, but isn’t [the Box-Cox transformation](https://en.wikipedia.org/wiki/Power_transform#Box%E2%80%93Cox_transformation) rather trivial to implement? Why do you need a package for this?

ML should be easy with a generic optimization library like Optim.jl. Eg see [a recent thread](https://discourse.julialang.org/t/maximum-likelihood-normal-linear-model/11664/20).

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