c(
person("Kosuke", "Imai", , "kimai@princeton.edu", c("aut", "cre")),
person("David", "van Dyk", , "dvandyk@imperial.ac.uk", c("aut")),
person("Jin", "Hubert", , "hubertj@princeton.edu", c("ctb"))
)
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>.
Kosuke Imai [aut, cre],
David van Dyk [aut],
Jin Hubert [ctb]

c(
person("Kosuke", "Imai", , "kimai@princeton.edu", c("aut", "cre")),
person("David", "van Dyk", , "dvandyk@imperial.ac.uk", c("aut")),
person("Hubert", "Jin", , "hubertj@princeton.edu", c("ctb"))
)
Kosuke Imai [aut, cre],
David van Dyk [aut],
Hubert Jin [ctb]

c(
person("Kosuke", "Imai", , "imai@harvard.edu", c("aut", "cre")),
person("David", "van Dyk", , "dvandyk@imperial.ac.uk", c("aut")),
person("Hubert", "Jin", , "hubertj@princeton.edu", c("ctb"))
)
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>.