Rules-of-thumb doc for mathematical programming in R?

Does there exist a simple, cheatsheet-like document which compiles the best practices for mathematical computing in R? Does anyone have a short list of their best-practices? E.g., it would include items like:

1. For large numerical vectors x, instead of computing x^2, one should compute x*x. This speeds up calcula开发者_如何转开发tions.
2. To solve a system $Ax = b$, never solve $A^{-1}$ and left-multiply $b$. Lower order algorithms exist (e.g., Gaussian elimination)

I did find a nice numerical analysis cheatsheet here. But I'm looking for something quicker, dirtier, and more specific to R.

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@Dirk Eddelbeuttel has posted a bunch of stuff on "high performance computing with R". He's also a regular so will probably come along and grab some well-deserved reputation points. While you are waiting you can read some of his stuff here: http://dirk.eddelbuettel.com/papers/ismNov2009introHPCwithR.pdf

There is an archive of the r-devel mailing list where discussions about numerical analysis issues relating to R performance occur. I will often put its URL in the Google advanced search page domain slot when I want to see what might have been said in the past: https://stat.ethz.ch/pipermail/r-devel/