Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) for examples and details of estimation.
Estimates conditional binary quantile models developed by Lu (2020). The estimation procedure is implemented based on Markov chain Monte Carlo methods.
Estimates finite quantile mixture models using Markov chain Monte Carlo methods. The finite quantile mixture models include both fixed- and random-quantile specifications that are applicable to both continuous and binary dependent variables.