Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. Now almost entirely superseded by the models-by-example repo.
This is the accompanying code repository for the AISTATS 2022 publication p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets by Alexander Munteanu, Simon Omlor and Christian Peters.
Étude de marché sur les personnes âgées qui souscrivent une assurance complémentaire aux États-Unis en réalisant des modèles probit et logit pour identifier les variables importantes.
Additional R scripts to produce data analysis and wrangling. You will need to run the main file here to get the manipulated dataframe that I use in the hbc-ctol repository. Here, some analysis that I did initially can also be found. The complete development environment for the stuff that you see on RPubs is also here. In memory of the late Jorge Pazmiño (1941-2021).