Last updated: 2018-11-06

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This repository contains source code and data accompanying our manuscript,

Zhengrong Xing and Matthew Stephens (2016). Smoothing via Adaptive Shrinkage (smash): denoising Poisson and heteroskedastic Gaussian signals. arXiv:1709.10066.

The new methods can be found in the smashr package.

Contents

The following analyses generate some of the empirical results presented in [Xing & Stephens (2016)][smashr-preprint]. If you encounter a problem running any of the R code in these examples, please post an issue.

Citing this work

If you find any of the source code in this repository useful for your work, please cite our paper:

Zhengrong Xing and Matthew Stephens (2016). Smoothing via Adaptive Shrinkage (smash): denoising Poisson and heteroskedastic Gaussian signals. arXiv:1709.10066.

License

Copyright (c) 2016-2018, Zhengrong Xing, Peter Carbonetto & Matthew Stephens.

All source code and software in this repository are made available under the terms of the MIT license. See the LICENSE file for the full text of the license.

Credits

This project was developed by Zhengrong Xing at the University of Chicago, with support and contributions from Peter Carbonetto and Matthew Stephens.


This reproducible R Markdown analysis was created with workflowr 1.1.1