A little review of my current Safari session since it is probably time to close all those tabs bookmarked on my iPhone.
I have long been aspiring to some kind of digital minimalism, and I think I am almost there, with my using only essential applications–to write, code, and read–and almost very reliable ones–Vim or Emacs, a Terminal, and macOS builtin and optimized apps. I would have a harder time to live without my bookshelf or my 25-year old collection of records. However, it seems that we can, provided you live like a real bohemian. (I came across this by reading one of Irreal blog post recently.)
I don’t use Chrome, and I don’t think it is a good idea to use any Google products by now. Facebook is just a little toy compared to the power that Google has accumulated over years. I am using a Mac and I subscribed to Apple Music, though, and you could retort that it’s all the same. I don’t think so, since it’s not the same offer of services, unless you think of Chromebook (or Android) users. Anyway, here is a little joy for Lisp saavy users: Dinosaur and Lisp. Note, however, that you will need to have a working Chrome(ium).
I didn’t knwo there was such a thing, but here it is: Tutorial on Good Lisp Programming Style, by Peter Norvig (PDF, 116 pp.). The Little Book of Python Anti-Patterns also provides good tips regarding Python this time.
Some fresh articles for the interested scientific reader:
- Please Stop Permuting Features: An Explanation and Alternatives: An interesting take on why permutation-based statistics may lead to erroneous assessment of model accuracy, especially in the case of strongly correlated predictors;
- Advances in epigenetics link genetics to the environment and disease: A great review of epigenetics and its role/implication in biology and health-related research;
- SciPy 1.0—Fundamental Algorithms for Scientific Computing in Python: Surely a great read for people interested in scientific computing, at least for the historical side and the resulting popularity of Python in data science and computational statistics;
- Lagged Explanatory Variables and the Estimation of Causal Effect (PDF, 15 pp.): In which the uthors used DAGs and Monte Carlo simulations to demonstrate that lag identification often leads to incorrect inferences;
- Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning: All you need to known about GLM vs. ML for prediction and causal inference, as a complement to Frank Harrell’s excellent past discussion of this hot topic.
Do you like quines? How about making compressed file quines, step by step? This blog post provides a nice description of how GZIP files are encoded and how they can be manipulated using Python.
Vim vs Emacs: Detailed Comparison, or the Holy war again. Interesting post, albeit a little too generalist for my taste (thx to Irreal!). On a related side, I happened to read a nice post on the history of Vim recently.
Program Design by Calculation is a (draft) textbook all about functional programming, functors and monads, and Haskell:
Functional programming has a tradition of absorbing fresh results from theoretical computer science, algebra and category theory. Languages such as Haskell have been competing to integrate the most re- cent developments and therefore are excellent prototyping vehicles in courses on program calculation, as happens with this book.
You may also like the related blog post: A Short Skinny on Relations & the Algebra of Programming.
Well, that’s all for this month!