`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.