RRM models and Software
The P-RRM model
The P-RRM model is one of the most recent member of the RRM models family. This model has a cornerstone meaning: it postulates the strongest random regret minimization behaviour possible within the RRM modelling framework. In fact, it is one of the two special limiting cases of the µRRM model (the other one is linear-additive RUM). The model has a number of favourable properties. Most notably, it is very fast to estimate in MNL form - which is especially beneficial in the context of large choice sets.
Click here to go to the P-RRM software page
The µRRM model
The µRRM model generalizes the Classical RRM model by allowing the variance of the error term to be estimated. More precisely, in the µRRM model we estimate the scale parameter µ, which is definitionally linked to the error variance.
Click here to go to the µRRM software page.
The RRM2010 model
Not recently proposed, but of course not to be forgotten here: the RRM2010 model. This model is proposed by Caspar Chorus (see Chorus 2010). So far, most RRM applications in the literature have used this model. See Chorus et al. 2014 for a recent overview for applications of this model.
Click here to go to the RRM2010 software page.
Latent class models
Latent class (LC) models are increasingly used in choice analysis, and are particularly suitable to investigate the existence of decision rule heterogeneity. In the context of advanced RRM models, it is particularly interesting to define classes corresponding to different decision models. In the latent class software page 3 example latent class models are presented:
1) a two-class model comprising of a RUM class and a P-RRM class.
2) a two-class model comprising of two μRRM classes.
3) a three-class model comprising of a RUM class, a P-RRM class and a μRRM class.
Click here to go to the Latent class software page.