Nim is not ready, even Python is missing several key R statistics packages.

I do try to implement what people find useful in Arraymancer, for example I 
implemented randomized SVD and [randomized 
PCA](https://github.com/mratsim/Arraymancer/pull/384)

With the results here: 
[http://home.chpc.utah.edu/~u6000771/somalier-ancestry.html](http://home.chpc.utah.edu/~u6000771/somalier-ancestry.html)

And while we are at it, I have some API RFC for column preprocessing: 
[https://github.com/mratsim/Arraymancer/issues/385](https://github.com/mratsim/Arraymancer/issues/385).
 How to indicate a common transformation to all stats like PCA or logistic 
regression:

  * enum 
    
          type FeaturePreprocessing = enum
        Auto
        NoPreprocessing
        MeanCentering
        MinMaxScaling
        StandardScaling
        RobustScaling
        ...
        
    
    Run

  * scaler object
  * scaler proc



Regarding exploration, Nim compiles fast, you can play with the following to 
run Nim in Jupyter: 
[https://github.com/apahl/nim_magic](https://github.com/apahl/nim_magic) 
(similar to cython in Jupyter).

Also lastly Status sponsored a developer to add hot-code reloading to Nim so 
that Nim code could be modified while running. Running in Jupyter is typically 
one of the use-cases, we just Nim someone to write the package: 
[https://nim-lang.org/docs/hcr.html](https://nim-lang.org/docs/hcr.html) (There 
are other experiments: 
[https://github.com/stisa/jupyternim](https://github.com/stisa/jupyternim))

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