mlogit: Multinomial Logit Models
Maximum likelihood estimation of random utility discrete
choice models. The software is described in Croissant (2020)
<doi:10.18637/jss.v095.i11> and the underlying methods in
Train (2009) <doi:10.1017/CBO9780511805271>.
Version:
1.1-3
Depends:
R (≥ 2.10),
dfidx
Published:
2025年07月12日
Author:
Yves Croissant [aut, cre]
Maintainer:
Yves Croissant <yves.croissant at univ-reunion.fr>
NeedsCompilation:
no
Documentation:
Vignettes:
2. Data management, model description and testing (
source,
R code)
3. Random utility model and the multinomial logit model (
source,
R code)
4. Logit models relaxing the iid hypothesis (
source,
R code)
5. The random parameters (or mixed) logit model (
source,
R code)
6. The multinomial probit model (
source,
R code)
7. Miscellaneous models (
source,
R code)
Exercise 1: Multinomial logit model (
source,
R code)
Exercise 2: Nested logit model (
source,
R code)
Exercise 3: Mixed logit model (
source,
R code)
Exercise 4: Multinomial probit (
source,
R code)
mlogit (
source,
R code)
Downloads:
Reverse dependencies:
Reverse suggests:
AER,
broom,
catdata,
choicedata,
DCEtool,
generalhoslem,
ggeffects,
gofcat,
insight,
lmw,
logitr,
marginaleffects,
micsr,
mixl,
mlogitBMA,
nonnest2,
performance,
RprobitB,
support.BWS,
urbin,
WeightIt
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