The GRRM model
This model is very recently proposed by Chorus 2014. This model generalises the RRM2010 model, and has  just as the µRRM model  beside the RRM2010 model also the linearadditive RUM model as a special case.
In the GRRM model the fixed constant of one in the attribute level regret function of the RRM2010 model is replaced by a regretweight variable denoted γ, see the equation below. γ can be estimated for each attribute seperately, or one can estimate the model using one generic γ.
MATLAB

Click here for a bundle of MATLAB codes, which includes code to estimate GRRMMNL models
BISON BIOGEME

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

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

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

Click here for Apollo R GRRM 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)
The figure on the right shows the effect of the size of γ on the attribute level regret function. As can be seen, a γ = 1 results in the Classical RRM model. As γ gradually increases the attribute level regret function becomes less convex. In the special case in which γ = 0, the GRRM model predicts the same choice behaviour as the linearadditive RUM model.