I have a draft chapter on Bayes stats and MCMC at https://oliviergimenez.github.io/banana-book/crashcourse.html I’d love your feedback about what is confusing and what is missing 😇 #rstats
So, I’m writing a book 🤯 It''’s called “Bayesian analysis of capture-recapture data with hidden Markov models - Theory and case studies in R”.
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.