index.Rmd

---
title: "SMASH companion source code repository"
site: workflowr::wflow_site
output:
  workflowr::wflow_html:
    toc: false
---

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][smash-preprint].

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][smashr].

## 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][mit-license]. See the
[LICENSE](LICENSE) file for the full text of the license.

## Contents

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

[**Illustration:** SMASH applied to the "spikes" data set.](spikesdemo.html)

### Comparisons of signal denoising methods in simulated data

1. [Estimating the mean function from Gaussian-distributed
data.](gausshomo.html)

2. Estimating the variance function from Gaussian-distributed data.

3. Poisson-distributed data.

### Illustrative applications

1. [Motorcycle acceleration data.](motorcycle.html)

2. [ChIP-seq data.](chipseq.html)

## 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][zhengrong] at the
University of Chicago, with support and contributions from
[Peter Carbonetto][peter] and [Matthew Stephens][matthew].

[smash-github]: https://github.com/stephenslab/smash-paper
[smashr]: https://github.com/stephenslab/smashr
[smash-preprint]: http://arxiv.org/abs/1605.07787
[issues]: https://github.com/stephenslab/smash-paper/issues
[mit-license]: https://opensource.org/licenses/mit-license.html
[zhengrong]: https://github.com/zrxing
[peter]: http://pcarbo.github.io
[matthew]: http://stephenslab.uchicago.edu