Last updated: 2018-05-15

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• ✔ 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: 388e65e

  Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
  
  Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

  
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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
html	4cca6ca	LSun	2018-05-14	Build site.
Rmd	36b5314	LSun	2018-05-14	wflow_publish(“analysis/about.Rmd”)
html	ddf9062	LSun	2018-05-12	Update to 1.0
Rmd	cc0ab83	Lei Sun	2018-05-11	update
html	0f36d99	LSun	2017-12-21	Build site.
html	853a484	LSun	2017-11-07	Build site.
Rmd	2a9b0b7	LSun	2017-03-30	weights
html	2a9b0b7	LSun	2017-03-30	weights
html	d575294	LSun	2017-01-31	Build site.
Rmd	23a60a2	LSun	2017-01-29	readme
html	23a60a2	LSun	2017-01-29	readme
Rmd	1d187aa	LSun	2017-01-29	First Commit
html	1d187aa	LSun	2017-01-29	First Commit

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Building on Prof. Matthew Stephens’s initial observation that under correlation, although the empirical distributions of the test statistics are different from their theoretical marginal one, correlation affacts test statistics unevenly. Moderate observations, defined as \(|\hat\beta / \hat s| \leq t\) with a pre-specified \(t\), are more prone to correlation and thus contain less information to control false discoveries than extreme ones, truncash makes partial use of moderate observations, combined with full use of extreme ones, for adaptive empirical Bayes shrinkage.

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This reproducible R Markdown analysis was created with workflowr 1.0.1

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