Anatomy of a logistic growth curve, by Tristan Mahr. Nice looking visualization and clearly a non mathy but well put explanation of the logistic curve. I wish I read this earlier, when I started teaching psychometrics. #rstats

Immersive Linear Algebra. #maths

Trying out Travis CI for a Bookdown project. I’m already at the third failure and it starts to be painful. #rstats

Linear algebra in Emacs using MKL and dynamic modules. #emacs

Look, I read two of the latest newletters by Sacha Chua and I already learned about two new Org features: org-reverse-datetree and org-bib-template. Moreover, I didn’t know that there were such thing as meta repository for ESS users. #emacs

I’m finally done with Occupied.

I think this is the first time this site is referenced in Sacha Chua excellent Emacs newsletter.

Mathematica implementations of machine learning algorithms used for prediction and personalization.

This open source project is for Mathematica implementations of statistical and machine learning algorithms that can be used for data analysis, prediction, and recommendation systems.

Note that the Github repository also includes Lua, Java and R code. The companion website is Mathematica for prediction algorithms.

A few days ago, I read a thread on Biostars (which I haven’t consulted in a while) on the use of Wolfram mathematica in bioinformatics, and I wondered why people are so critical of this software. The same applies to Stata (if you see the recent flame on Twitter, you know what I mean), albeit in this case there’s not even this man behind it.

Long time no see. I have been compiling several pieces of bioinformatics software lately. No issues whatsoever, except for a few glitch with boost libraries.