https://github.com/nignatiadis/ebayes.jl

Empirical Bayes shrinkage in Julia
https://github.com/nignatiadis/ebayes.jl

Last synced: 26 days ago
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Empirical Bayes shrinkage in Julia

• Host: GitHub
• URL: https://github.com/nignatiadis/ebayes.jl
• Owner: nignatiadis
• License: mit
• Created: 2019-10-22T23:00:36.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-10-08T00:49:33.000Z (about 4 years ago)
• Last Synced: 2024-11-11T08:44:09.336Z (about 2 months ago)
• Language: Julia
• Homepage: https://nignatiadis.github.io/EBayes.jl/dev
• Size: 487 KB
• Stars: 4
• Watchers: 3
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# EBayes.jl

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A Julia package for empirical Bayes estimation. See the [documentation](https://nignatiadis.github.io/EBayes.jl/dev) for instructions on how to use it.

The package implements the empirical Bayes cross-fit method [1], which estimates effect sizes of many experiments by optimally synthesizing experimental data and rich covariate information. Furthermore, the method may leverage any black-box predictive model: [1] provides theoretical guarantees that hold for *any* regression method and the package here allows usage of any supervised model that has implemented the [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) interface.

# References

[1] Ignatiadis, N., & Wager, S. (2019). Covariate-Powered Empirical Bayes Estimation. To appear in Advances in Neural Information Processing Systems 32 (NeurIPS 2019). [arXiv:1906.01611.](https://arxiv.org/abs/1906.01611)