Last updated: 2018-11-06
workflowr checks: (Click a bullet for more information) ✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Repository version: aa4437d
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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 | 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 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.
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.
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.
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.
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