  This paper introduces the logitr R package for fast maximum likelihood
estimation of multinomial logit and mixed logit models with unobserved
heterogeneity across individuals, which is modeled by allowing parameters to
vary randomly over individuals according to a chosen distribution. The package
is faster than other similar packages such as mlogit, gmnl, mixl, and apollo,
and it supports utility models specified with "preference space" or
"willingness to pay (WTP) space" parameterizations, allowing for the direct
estimation of marginal WTP. The typical procedure of computing WTP
post-estimation using a preference space model can lead to unreasonable
distributions of WTP across the population in mixed logit models. The paper
provides a discussion of some of the implications of each utility
parameterization for WTP estimates. It also highlights some of the design
features that enable logitr's performant estimation speed and includes a
benchmarking exercise with similar packages. Finally, the paper highlights
additional features that are designed specifically for WTP space models,
including a consistent user interface for specifying models in either space and
a parallelized multi-start optimization loop, which is particularly useful for
searching the solution space for different local minima when estimating models
with non-convex log-likelihood functions.
