mlogit: Multinomial Logit Models

Maximum Likelihood estimation of random utility discrete choice models, as described in Kenneth Train (2009) Discrete Choice Methods with Simulations <doi:10.1017/CBO9780511805271>.

Version: 1.1-0
Depends: R (≥ 2.10), dfidx
Imports: Formula, zoo, lmtest, statmod, MASS, Rdpack
Suggests: knitr, car, nnet, lattice, AER, ggplot2, texreg, rmarkdown
Published: 2020-05-26
Author: Yves Croissant [aut, cre]
Maintainer: Yves Croissant <yves.croissant at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
In views: Econometrics, SocialSciences
CRAN checks: mlogit results


Reference manual: mlogit.pdf
Vignettes: 2. Data management, model description and testing
3. Random utility model and the multinomial logit model
4. Logit models relaxing the iid hypothesis
5. The random parameters (or mixed) logit model
6. The multinomial probit model
7. Miscellaneous models
Exercise 1: The multinomial logit model
Exercise 2: The nested logit model
Exercise 3: The mixed effects logit model
Exercise 4: The multinomial probit model
Package source: mlogit_1.1-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: mlogit_1.1-0.tgz, r-oldrel: mlogit_1.1-0.tgz
Old sources: mlogit archive

Reverse dependencies:

Reverse depends: covLCA, mpbart, nopp, PHInfiniteEstimates
Reverse imports: clusterSEs, Demerelate, gmnl, idefix, misclassGLM, mnlogit, riskclustr
Reverse suggests: AER, catdata, catspec, cobalt, dfidx, generalhoslem, insight, McSpatial, mixl, mlogitBMA, nonnest2, performance, plot3logit, support.BWS, WeightIt
Reverse enhances: prediction, stargazer, texreg


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