Models for Discrete Choice Experiments
The package includes the following models:
- Multinomial (conditional) Logit (MNL)
- Mixed (random parameters) Logit (MXL)
- Generalized Multinomial Logit Model (GMXL)
- Latent Class (LC)
- Latent Class Mixed Logit (LCMXL)
- Multiple Indicators Multiple Causes (MIMIC)
- Hybrid Multinomial Logit (HMNL)
- Hybrid Mixed Logit (HMXL)
- Hybrid Latent Class (HLC)
The models are estimated using maximum likelihood method and work with the following specifications:
- preference or WTP space
- multiple distribution types (for random parameters)
- non-linear transformations of explanatory variables
- covariates of means, scale, and scale variance (where applicable)
- impose equality restrictions or constraints
- flexible data types (panel structure, non-constant number of choice tasks or alternatives per respondent, missing data)
- various estimation and numerical optimization algorithms and options
- parallel computing
- and more ...
