It has been month and a half since I compiled a list of statistical/programming internet flotsam and jetsam.

  • Via Lambda The Ultimate: Evaluating the Design of the R Language: Objects and Functions For Data Analysis (PDF). A very detailed evaluation of the design and performance of R. HT: Christophe Lalanne. If you are in statistical genetics and Twitter Christophe is the man to follow.
  • Attributed to John Tukey, link “without assumptions there can be no conclusions” is an extremely important point, sanitary which comes to mind when listening to the fascinating interview to Richard Burkhauser on the changes of income for the middle class in USA. Changes to the definition of the unit of analysis may give a completely different result. By the way, pregnancy does someone have a first-hand reference to Tukey’s quote?
  • Nature news publishes RNA studies under fire: High-profile results challenged over statistical analysis of sequence data. I expect to see happening more often once researchers get used to upload the data and code for their papers.
  • Bob O’Hara writes on Why simple models are better, which is not positive towards the machine learning crowd.
  • A Matlab Programmer’s Take On Julia, and a Python developer interacts with Julia developers. Not everything is smooth. HT: Mike Croucher. ?
  • Dear NASA: No More Rainbow Color Scales, Please. HT: Mike Dickinson. Important: this applies to R graphs too.
  • Rafael Maia asks “are programmers trying on purpose to come up with names for their languages that make it hard to google for info?” These are the suggestions if one searches Google for Julia:

    Unhelpful search suggestions.

  • I suggest creating a language called Bieber and search for dimension Bieber, loop Bieber and regression Bieber.

That’s all folks.