JuliaCon 2023

Comparison of Choice models in Julia, Python, R, and STATA
07-27, 15:30–16:00 (US/Eastern), 26-100

The proposal is about which Covid vaccine would be selected by target group of patients in a cross cultural comparison of US and India as features related to the vaccines vary. The data is analyzed using packages from Julia, Python, R, and STATA. The objective is to present a comparison of tools while analyzing an extremely relevant and time critical data related to Covid vaccinations


As the initial vaccines became available in the fight against Covid, data were collected from a total of 800 patients (395 in US, 405 in India) on their preference for the different Vaccines available. Key attributes related to efficacy, safety, dosing frequency, boosters, and cost were considered to identify the key factors and their levels which drove patients' choice in selecting the vaccine. Depending upon their interest, domain, and exposure, Julia, Python, R and STATA are the preferred choices for advanced modeling by both academic and professional modelers and researchers. The proposal provides a how-to in Discrete Choice Modeling and a comparison of results using each of these languages and tools.

A multi-disciplinary applied researcher with interest in data analysis tools and platforms such as Julia, Python, R, STATA, SAS, and SPSS. Interested in using GLMs, GLMMs, Bayesian methods, machine learning, and time series forecasting in bioinformatics, consumer goods, epidemiology, healthcare, marketing, and media mix optimization.