π I updated the slides of my introduction to #GIS and #mapping in #rstats using the #sf package and π» πΊοΈ in the #pyrenees ποΈ π§βπ« Slides: https://t.co/mO4Dg8l1H5
π§βπ» Code: https://t.
Happy to share lecture slides & R codes of an earlier version of the workshop π we ran back in 2014 π€― w JD Lebreton & @KoonsLab β‘οΈ https://t.co/XdcCQ560Pz #RStats For video π½οΈπΊ, check out our workshop on pop dynamics https://t.
π’π₯³ With @MaudQueroue and @ValentinLauret we gave a short introduction to nimble @R_nimble nimble is a #rstats π¦ 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 πhttps://t.co/Tgo6OJs7r0#OpenAccess #ReproducibleResearch #RStats π§΅β¬οΈ https://t.
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 π #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.