# Conclusions

mlogit estimates a large set of random utility models with a unified and user-friendly interface. Some of these models haven’t been presented in this article, namely:

• the rank-ordered logit model which is relevant in situations where individuals don’t choose one among a set of mutually exclusive alternatives, but rank them,
• the overlapping nested logit model which is a nested logit model where some alternatives may belongs to more than one nest,
• the paired combinatorial logit model which is a nested logit model where every possible combinations of two alternatives is a nest,
• the multinomial probit model, obtained when the errors are assumed to follow a multivariate normal distribution.

All these models are illustrated in the "mlogit" vignette of the mlogit package.

Moreover, mlogit provides useful functions and methods which compute and return useful results, like predicted probabilities, inclusive values, marginal effects, consumer surplus and individual parameters.