rstats

I would like to familiarize myself with machine learning (ML) techniques in R. So I have been reading and learning by doing. I thought I’d share my experience for others who’d like to give it a try1.

I use R to retrieve some data from Twitter, do some exploratory data analysis and visualisation and examine a network of followers.

Slides and codes available from https://sarahbauduin.github.io/formation_IBM_NetLogoR/.

Everything (including data, slides, codes and video recordings) about the workshop we run on Bayesian capture-recapture inference with hidden Markov models in R and Nimble.

Everything (including data, slides and codes) about the workshop on Bayesian statistics I run.

Everything (including data, slides and codes) about the workshop on reproducible science I run.

Bayesian implementation of Pollock’s robust design capture-recapture models w/ Jags.

In our weekly group meeting this morning, I introduced R Markdown a great #rstats tool to write reproducible documents (reports, articles, slides, websites) smoothly mixing text, code, figures and equations in html, word or pdf format.

In a recent seminar, S. Gandon (w/ S. Lion and T. Day) used SIR models to illustrate the effect of diff strategies of #SocialDistancing on the #COVID19 epidemic. What better way to learn than to reproduce their results in R?

It is my pleasure to announce that Michael Schaub and Marc Kéry will run their Integrated Population Modelling workshop in Montpellier this November.