Willingness to pay estimates from preference & WTP space utility models: the logitr package

My talk for the 2021 Sawtooth Conference Turbo Choice Modeling Panel.
Note that some of the functions and / or arguments in the logitr package have changed with newer updates to the package compared to what was shown in this video.
Related links:
My website: www.jhelvy.com/
logitr documentation: jhelvy.github....
logitr source code: github.com/jhe...

[@investigaonline]

Great!!! I have learn and understood the basis and the development od discrete choice modelling. A lot of thanks for this work. I will work with cbcTools and logitr in the future ...

[@dc33333]

Incredible!!! Very useful. Every data scientist/engineer should see his..

[@mima9416]

Hello, thank you for this package that seems to adress some issues in a very straightforward way. I am trying to use this package for some choice experiment data. However, my data have multiple choice tasks. In other words, each individual will make several choices. I calculated obsID = "ID_task" and not obsID = "ID_resp". I calculated the models using both mlogit and logitr, but with logitr a art from price the rest of the variables is not significant. Therefore, results are completely different. I assume there might be a problem with my assumption. Any idea about how to resolve the issue with multiple chocie tasks? Thanks in advance for your help. Best

[@JohnHelveston]

obsID is a sequence of repeated numbers that identifies each unique choice observation. See more here: jhelvy.github.io/logitr/articles/data_formatting.html

[@christiansetzkorn6241]

amazing thanks!