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This will probably be a non-trivial example to reproduce, owing to: (1) long-running code, (2) dependency on external data sources, (3) possibly challenging software dependencies -- both trivial ones (e.g. setting up custom fonts and plot themes) and critical ones (requires an external R package wrapping a C++ algorithm, not available on CRAN and can sometimes have interesting compiler issues, like when Apple decided to break the clang compiler in 10.0). Ideally one could just run the R code given in the appendix on your local R session, but that may take a bit of effort. We've tried to take steps to address those issues by providing caches of slow-running parts, providing a docker container, and providing sufficient annotations, but who knows!
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