README.md

R-Training

To cite, use:

This repository contains the full material I use in my teaching at the Université de Paris at various levels (undergraduate and Masters' degree).

If using any code from here, please make sure to cite the page!

It contains the following material

1. An introduction to R notebook
2. Introduction to the Tidyverse notebook images folder
3. Visualisation with the Tidyverse and ggplot2 notebook
4. Introduction to inferential statistics (correlations, and linear model) notebook
5. Slightly more advanced inferential statistics: notebook dataset1 dataset2 dataset3
   1. Linear model (for numeric outcomes)
   2. Logistic regression (for binomial categorical outcomes),
   3. Signal Detection Theory (accuracy, precision, sensitivity, specificity, d prime, etc...)
   4. Cumulative logit regression (for ordered categorical predictors obtained from Likert scales)
   5. Linear Mixed-effects modelling
   6. Principal Component Analysis (PCA)
   7. Decision Trees
   8. Random Forests grown via: i) conditional inference trees, ii) permutation tests as implemented in ranger and iii) following the tidymodels approach

Get in touch if there are any issues!