MNP: Fitting the Multinomial Probit Model

Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. <doi:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. <doi:10.18637/jss.v014.i03>.

Version: 3.1-5
Depends: R (≥ 2.1), stats, utils
Suggests: testthat
Published: 2024-06-20
DOI: 10.32614/CRAN.package.MNP
Author: Kosuke Imai [aut, cre], David van Dyk [aut], Hubert Jin [ctb]
Maintainer: Kosuke Imai <imai at harvard.edu>
BugReports: https://github.com/kosukeimai/MNP/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/kosukeimai/MNP
NeedsCompilation: yes
Materials: ChangeLog
In views: Bayesian, Econometrics
CRAN checks: MNP results

Documentation:

Reference manual: MNP.pdf
Vignettes: MNP

Downloads:

Package source: MNP_3.1-5.tar.gz
Windows binaries: r-devel: MNP_3.1-5.zip, r-release: MNP_3.1-5.zip, r-oldrel: MNP_3.1-5.zip
macOS binaries: r-release (arm64): MNP_3.1-5.tgz, r-oldrel (arm64): MNP_3.1-5.tgz, r-release (x86_64): MNP_3.1-5.tgz, r-oldrel (x86_64): MNP_3.1-5.tgz
Old sources: MNP archive

Reverse dependencies:

Reverse suggests: WeightIt
Reverse enhances: prediction

Linking:

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