R

Introduction on data wrangling with R

πŸ”§πŸš§ Intro on data wrangling w/ R πŸ“¦ dplyr - import data, pipe, pivot, join, filter, count, work w/ columns, dates, char, factors, etc #tidyverse ▢️ https://t.co/Vs2TbYhbBj ◀️ w/ practicals https://t.

Introduction to GIS and mapping in R

πŸ˜‡ I updated the slides of my introduction to #GIS and #mapping in #rstats using the #sf package and 🐻 πŸ—ΊοΈ in the #pyrenees πŸ”οΈ πŸ§‘β€πŸ« Slides: https://t.co/mO4Dg8l1H5 πŸ§‘β€πŸ’» Code: https://t.

Matrix population models: Lecture slides and R codes for a workshop

Happy to share lecture slides & R codes of an earlier version of the workshop πŸ‘‡ we ran back in 2014 🀯 w JD Lebreton & @KoonsLab ➑️ https://t.co/XdcCQ560Pz #RStats For video πŸ“½οΈπŸ“Ί, check out our workshop on pop dynamics https://t.

Bayesian analyses made easy: GLMMs in R package brms

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

Introductory workshop on Nimble

πŸ“’πŸ₯³ With @MaudQueroue and @ValentinLauret we gave a short introduction to nimble @R_nimble nimble is a #rstats πŸ“¦ to fit models in the Bayesian framework w/ MCMC, it's also a programming environment for using/coding fns/distns/samplers

New paper on trade-offs between Deep Learning for species id inference on predator-prey co-occurrence

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 πŸ˜‡https://t.co/Tgo6OJs7r0#OpenAccess #ReproducibleResearch #RStats πŸ§΅β¬‡οΈ https://t.

Binary image classification using Keras in R: Using CT scans to predict patients with Covid

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.

Draft chapter on Bayes stats and MCMC in R

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 πŸ˜‡ #rstats

Bayesian analysis of capture-recapture data with hidden Markov models - Theory and case studies in R

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”.

Experimenting with machine learning in R with tidymodels and the Kaggle titanic dataset

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.