R

🔧🚧 Intro on data wrangling w/ R 📦 dplyr - import data, pipe, pivot, join, filter, count, work w/ columns, dates, char, factors, etc #tidyverse ▶️ https://t.co/Vs2TbYhbBj ◀️ w/ practicals https://t.

😇 I updated the slides of my introduction to #GIS and #mapping in #rstats using the #sf package and 🐻 🗺️ in the #pyrenees 🏔️ 🧑‍🏫 Slides: https://t.co/mO4Dg8l1H5 🧑‍💻 Code: https://t.

Happy to share lecture slides & R codes of an earlier version of the workshop 👇 we ran back in 2014 🤯 w JD Lebreton & @KoonsLab ➡️ https://t.co/XdcCQ560Pz #RStats For video 📽️📺, check out our workshop on pop dynamics https://t.

Here I illustrate how to fit GLMMs with the R package brms, and compare to Jags and lme4.

📢🥳 With @MaudQueroue and @ValentinLauret we gave a short introduction to nimble @R_nimble nimble is a #rstats 📦 to fit models in the Bayesian framework w/ MCMC, it's also a programming environment for using/coding fns/distns/samplers

A pleasure to work w/ #ComputoJournal @Computo85445972 for our paper on trade-offs bw #DeepLearning for species id & inference on predator-prey co-occurrence, which comes w/ a reproducible R workflow 😇https://t.co/Tgo6OJs7r0#OpenAccess #ReproducibleResearch #RStats 🧵⬇️ https://t.

Here I illustrate how to train a CNN with Keras in R to predict from patients' CT scans those who will develop severe illness from Covid.

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.