Last updated: 2018-11-06

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This repository contains data and R source code accompanying our manuscript,

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

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

The new methods can be found in the smashr package.

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.

Contents

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

What’s included in the git repository

See here for the source repository. This is what you will find in the repository:

Fill out this section.

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