(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Anthony Goldbloom writes:
In late August, Kaggle launched an open data platform where data scientists can share data sets. In the first few months, our members have shared over 300 data sets on topics ranging from election polls to EEG brainwave data. It’s only a few months old, but it’s already a rich repository for interesting data sets.
It’s also a nice place to share reproducible data science. We have built a tool called Kaggle Kernels, which allows data scientists and statisticians to share notebooks and scripts in Python or R on top of the data. If you find analysis you want to extend, you can “fork it” which gives you a reproducible version without going through the pain of replicating the author’s environment. It’s useful for learning new techniques (by being able to fork and play with other’s code), to share your side project with a large community and to draw attention to your research and store it in a way that can be easily reproduced.
He adds:
We don’t support Stan yet but we inevitably will.
Sooner rather than later, I hope!
P.S. Jamie Hall of Kaggle writes:
We’ve got RStan and PyStan ready to go in Kernels now. It would be fantastic to see some examples of the best ways to use them.
P.P.S. Aki has made a Kaggle notebook Bayesian Logistic Regression with rstanarm, and it works just fine.
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