The PRRM model
The PRRM model postulates the strongest random regret minimization behaviour possible within the RRM modelling framework. It is one of the two special limiting cases of the µRRM model. See Cranenburgh et al. 2015 for an extensive description of the model.
The key idea behind this model is that no rejoice (i.e. the opposite of regret) is experienced when the considered alternative outperforms a competitor alternative with regard an attribute m. This, in contrast to the Classical RRM model and the µRRM model which both postulate that regrets and rejoices are experienced. The figure on the right depicts attribute level regret function of the PRRM model.
To estimate a PRRM model, we need to compute the socalled PRRM Xvector, denoted xPRRM. Because the model is essentially linear this can be done prior to estimation. A prerequisite to do so however is that the signs of the taste parameters are known prior to estimation  which is typically the case. Once we have the PRRM Xvector, estimation of the PRRM model is just as simple and fast as estimation of a linearadditive RUM model.
The linearadditive form of the PRRM model also makes it very attractive from a computational perspective. As the Xvector can be computed prior to the estimation, runtimes of the PRRM model are proportional with choice set size, as opposed to quadratic  which is the case for the other RRM models.
MATLAB

Click here for a bundle of MATLAB codes, which includes:

Code to compute PRRM Xvectors

Code to estimate PRRMMNL models

BISON BIOGEME

Click here for BISON BIOGEME PRRM estimation code to estimate shopping choice data.
PYTHON BIOGEME

Click here for PYTHON BIOGEME PRRM estimation code to estimate shopping choice data.
PANDAS BIOGEME

Click here for PANDAS BIOGEME PRRM estimation code to estimate shopping choice data.
Apollo R

Click here for Apollo R PRRM estimation code to estimate shopping choice data.
EXAMPLE DATA FILE

Click here to download the example shopping choice data file (see Arentze et al. 2005 for more details on the data)