Modeling of choice behavior based on Random Utility Theory implemented using Python.
- Multinomial Logit Model (MNL)
- Nested Logit Model (NL)
- TasteNet-MNL [Han+, 2020]
For Mixed Logit Model (MXL) and Latent Class Logit Model (LCCM), I recommend using the Python package xlogit or the R packages mlogit and flemix.
- Tetsuro Hyodo. "How to estimate discrete choice models with R" https://www2.kaiyodai.ac.jp/~hyodo/Logit_by_R.pdf
- Han, Yafei, Christopher Zegras, Francisco Camara Pereira, and Moshe Ben-Akiva. 2020. “A Neural-Embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability.” arXiv [econ.EM]. arXiv. http://arxiv.org/abs/2002.00922.