Discrete choice modeling in Python with large datasets & single choice / basket models - Assortment & Pricing Optimization .

Inverse Stochastic User Equilibrium with LOGIT assignment

Advanced choice modeling with multidimensional utility representations.

Lab experiment implemented with PyQT to study the impact of time perception on route choice decisions. It presents choice scenarios using animations or numerical attributes.

Binary logit based on the Mobility and Transport Microcensus 2015 explaining the "choice" of working - at least from time to time - from home in Switzerland.

Introducing the Apollo Choice Modelling for Penalty Shot Prediction GitHub repository. Utilizing Apollo choice modeling, our code predicts soccer penalty shots. Featuring a user-friendly R Shiny app, the model considers player data, shot locations, and goalkeeper behavior to estimate shot success.

Structural modeling and estimation in JAX and Equinox.

The model that has been uploaded to this repository aspires to describe routing behavior of micro-mobility modes, e.g., e-bikes and e-scooters, in relationship with traditional modes, e.g., private car and walking.

This repository contains R code that analyzes data gathered from a lab experiment that studies the influence of time perception on route choices in public transport

Extended discrete choice modeling notebook with Fair's affair data & STAR98 education analysis. Covers Logit, Probit, GLM, diagnostics, model comparison, marginal effects, and advanced topics like censored regression and count models. Complete with visualizations and validation techniques.

This was the code I use to process a Multinomial Logistic Regression on R, with the Apollo Choice Modeling Package for R. Used to calculate the utility function of particular customers of vehicles in Bogotá, Colombia

A Collection of Utility Functions for Choice Modeling and Statistical Analysis

The goal of simulateDCE is to make it easy to simulate choice experiment datasets using designs from NGENE, idefix or spdesign.