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))