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

Computational graph-based discrete choice models

Estimation of Gaussian Process - Latent Class Choice Models (GP-LCCM) using the Expectation Maximization (EM) algorithm

This library simulates the inventory control problem of perishable products by means of Discrete Choice Methods (DCM)

Supplementary Code and Data for "Network Formation and Dynamics among Multi-LLMs"

Estimation of Gaussian Bernoulli Mixture - Latent Class Choice Models (GBM-LCCM) using the Expectation Maximization (EM) algorithm

Python implementation of Multinomial Logit Model

Provide a Stated Preference (SP) Survey analysis focusing separately on experimental design, questionnaire creation and SP econometrical analysis

This work pertains to detailed analysis, through survey in and around Kolkata region, of the existing Multi-Dimensional Poverty Index used by India to calculate the level of poverty at individual, state and nation level.

This is an AI-powered modeling framework developed to forecast restaurant demand, classify popularity tiers, and simulate visitor decision-making in urban environments. The project combines LSTM, Kolmogorov–Arnold Networks (KAN), and Discrete Choice Modeling (DCM) to deliver predictive and interpretable insights for smart city planning, operations.