Last updated: 2020-06-23

Checks: 2 0

Knit directory: genes-to-foodweb-stability/

This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

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:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    code/.Rhistory

Untracked files:
    Untracked:  figures/rich-geno-critical-transition.pdf

Unstaged changes:
    Modified:   figures/rich-geno-critical-transition-v4.pdf
    Modified:   output/critical-transitions.RData

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.


These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 86116c8 mabarbour 2020-06-23 bioRxiv version of code and data.
html 86116c8 mabarbour 2020-06-23 bioRxiv version of code and data.
html 28c2a9f mabarbour 2020-02-05 Build site.
Rmd 27ad8e2 mabarbour 2020-02-05 Start workflowr project.

Statistical analysis of critical transitions and resulting food webs

Bayesian multivariate autoregressive model and structural stability

Statistical analysis of plant growth in absence of insects