SMASH companion source code repository

Last updated: 2018-11-08

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✔ Repository version: 1b61bd1

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	1b61bd1	Peter Carbonetto	2018-11-08	wflow_publish(“index.Rmd”)
Rmd	58539f5	Peter Carbonetto	2018-11-08	wflow_publish(“index.Rmd”)
Rmd	f4372ca	Peter Carbonetto	2018-11-08	Moved around some of the files, and revising the home page.
html	b5453fa	Peter Carbonetto	2018-11-06	Small edit to home page.
html	4c88a8b	Peter Carbonetto	2018-11-06	A few more fixes to the home page.
Rmd	7caace6	Peter Carbonetto	2018-11-06	wflow_publish(“index.Rmd”)
Rmd	4baa137	Peter Carbonetto	2018-11-06	wflow_publish(“index.Rmd”)
html	0328612	Peter Carbonetto	2018-11-06	Re-built the home page after revamping it.
Rmd	aa4437d	Peter Carbonetto	2018-11-06	wflow_publish(“index.Rmd”)
Rmd	27bb547	Peter Carbonetto	2018-11-06	Greatly simplified the README.
html	0a6b926	Peter Carbonetto	2018-10-18	Adjusted workflowr site rendering.
html	c0faf17	Peter Carbonetto	2018-10-18	Added links to analyses in home page.
Rmd	40ad2f2	Peter Carbonetto	2018-10-18	wflow_publish(“index.Rmd”)
html	0797696	Peter Carbonetto	2018-08-23	Build site.
Rmd	80e51ae	Peter Carbonetto	2018-08-23	Start workflowr project with wflow_start().

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

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.

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.

Illustration

SMASH applied to the “spikes” data set.

Simulation experiments

Estimating the mean function from Gaussian-distributed data.
Estimating the variance function from Gaussian-distributed data.
Poisson-distributed data.

Illustrative applications

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