Last updated: 2018-11-08
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✔ Repository version: f4372ca
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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.
File | Version | Author | Date | Message |
---|---|---|---|---|
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.
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.
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.
**Comparisons of signal denoising methods in simulated data:**
Poisson-distributed data.
Illustrative applications:
See here for the source repository. This is what you will find in the repository:
Fill out this section.
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