Monte Carlo integration and named draws | Bayesian estimation | Piecewise, Box--Cox, and symbolic derivatives | MDCEV estimation and forecasting | Catalogs, segmentation, and assisted specification | Sampling of alternatives | Hybrid-choice specifications | Equivalence and reproducibility checklist

Installation and Python configuration | Data and expressions | A complete multinomial logit model | Choosing an output directory | Generic likelihoods and simulations | Database operations | Reproducible output | Finding the right starting point

A reusable example database | Multinomial logit with availability, codes, and weights | Nested and cross-nested logit | Panel likelihoods | Simulation and diagnostics | Fresh estimation and explicit result loading | Controlling native behavior

Installation and Python configuration | Data and expressions | A complete multinomial logit model | Choosing an output directory | Generic likelihoods and simulations | Database operations | Reproducible output | Finding the right starting point

A reusable example database | Multinomial logit with availability, codes, and weights | Nested and cross-nested logit | Panel likelihoods | Simulation and diagnostics | Fresh estimation and explicit result loading | Controlling native behavior

Monte Carlo integration and named draws | Bayesian estimation | Piecewise, Box--Cox, and symbolic derivatives | MDCEV estimation and forecasting | Catalogs, segmentation, and assisted specification | Sampling of alternatives | Hybrid-choice specifications | Equivalence and reproducibility checklist