Advanced Random Regret Minimization Models

Hi all,

Welcome to my website. Often I encounter new models or estimation techniques in the scholarly literature which are potentially useful for me. However, the burden to code that new model can be high: it simply requires time. With this website I hope to lower that burden for researchers and practitioners interested to see whether recent RRM models and methods can be useful for them.

 

Under the tab 'RRM models & Software' you can find code to estimate:

• P-RRM

• μRRM

• G-RRM

• RRM2010

• RRM2008

• Latent class models, comprising of RUM and RRM classes

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Software code is available for commonly software packages: 

• PANDAS BIOGEME

• Apollo R

• PYTHON BIOGEME

• BISON BIOGEME

• MATLAB

• LatentGOLD CHOICE

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Under the tab 'More RRM methodology' you can find recently developed RRM methodology, dealing with:

• Software for creating robust efficient stated choice experimental designs (new)

• Profundity of regret

• Accounting for variation in choice set size in RRM models

• Robustness of RRM modelling outcomes towards omitted attributes

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Furthermore, for those visitors who are not here for estimation code, in the 'About' tab and 'Publications' tab you can find out more about my research.