I didn’t read The Book of Why, but I heard of it a lot on Twitter lately. As you may know, I’m a big fan of Stephen Senn’s work, and I keep following his posts here and there even if I’m no longer being involved in medical statistics. Here is another nice dicussion of Lord’s paradox, where the author explains why neglecting random effects may affect the conclusions drawn from a study. If you’re interested in causal modeling of pre-post study, take a further look at this recent paper: Causal Graphical Views of Fixed Effects and Random Effects Models.

Another nice R⁴ article by Dirk Eddelbuettel on Debugging with Docker and Rocker. #rstats

Ode.io: A simple personal publishing engine for the open web.

If not SICP, then what? Maybe HTDP?.

TIL about “earmuffs” (I follow this convention but didn’t know it has a proper name!). #lisp

Programming Algorithms: Data Structures. #lisp

Pretext (formerly, Mathbook XML): An uncomplicated XML vocabulary for authors of research articles, textbooks, and monographs.

  Little playlist feat. Joy Division.

Joy Division raised desolation to the level of high art, and they covered plenty of stylistic ground while doing so. They could be severe: “Warsaw” is punishing, three-chord punk, bitter and mean. They could be chilly: “Heart and Soul” dwells in an ice cavern of echo. And they could be desperate, as on “Something Must Break”–a harrowing glimpse into the darkness that would be the band’s undoing.

A Stratification Approach to Partial Dependence for Codependent Variables: code and paper.

Free Book: Foundations of Data Science (from Microsoft Research Lab). We’re definitely going from surprise to surprise with Microsoft in the last few months. At least they are moving in the right direction with Github, VS Code and their open-source projects.