mako: r (72)

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  1. I think I hate these.
  2. "R provides Type I sequential SS, not the default Type III marginal SS reported by SAS and SPSS. In a nonorthogonal design with more than one term on the right hand side of the equation order will matter (i.e., A+B and B+A will produce different results)! We will need use the drop1( ) function to produce the familiar Type III results."
  3. updated: 2015-05-19, original: 2015-05-19 to , , , , , , , , , - Archived Link
  4. ggplot2 in Python. Awesome.
  5. Math and biostats are the shortest. History is the longest. Nobody is suprised.
  6. Proprietary R with a bunch of go-fast stuff for big data.
  7. updated: 2013-01-02, original: 2012-11-21 to , , , - Archived Link
  8. OK. That is incredible.
  9. Wow. Cool looking.
  10. Includes a pretty interesting discussion of dealing with overdispersion in mixed-effects models (e.g., count models) by using individual-level fixed effects. It also includes a whole series of citations.
  11. 2012-04-04 to , , , , , - Archived Link
  12. Cute way of creating web visualizations in R that don't work in my new NoScript-enabled browser.
  13. A bunch of R code to fit your power law distributions.
  14. updated: 2012-04-04, original: 2011-10-16 to , , , , , , - Archived Link
  15. Another cool looking tool from The King.
  16. I can't believe I haven't been using this package.
  17. Presentation on how to use MapR.
  18. I'm a huge fan of data.table and I've used it a ton in my own work in the last year.
  19. Very cool.
  20. There should be a better way of finding your R package than searching though a page of short descriptions of all 2800 package.s But there isn't.
  21. Answer: When considering which class to use, always choose the least complex class that will support the application. That is, use Date if possible, otherwise use chron and otherwise use the POSIX classes.
  22. OK. That's pretty cool.
  23. It's exactly what it sounds like. Good? Slightly horrifying?
  24. Awesome (and very short) section of debugging: 1. Be liberal with the use of print (or cat()) statements in your functions when debugging them! 2. traceback() # can see the sequence of function calls 3. options(error = dump.frames) debugger() # permits you to see the values of objects in the various nested environments of the function calls
  25. Emacs
  26. A new R module that lets you keep your data on disk.
  27. Lots of good advice on how to manage your R project.
  28. The description of fitting coxph to time dependent data here seems to be better than any I've found yet.

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