Package: MNP
Version: 2.3-6
Date: 2006-1-11
Title: R Package for Fitting the Multinomial Probit Model
Author: Kosuke Imai <kimai@princeton.edu>,
David A. van Dyk <dvd@uci.edu>.
Maintainer: Kosuke Imai <kimai@princeton.edu>
Depends: R (>= 2.1), MASS
Description: MNP is a publicly available R package that 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
software 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.
LazyLoad: yes
LazyData: yes
License: GPL (version 2 or later)
URL: http://imai.princeton.edu/research/MNP.html
Packaged: Wed Jan 11 09:39:23 2006; kimai

Package: MNP
Version: 2.3-7
Date: 2006-2-24
Title: R Package for Fitting the Multinomial Probit Model
Author: Kosuke Imai <kimai@princeton.edu>,
David A. van Dyk <dvd@uci.edu>.
Maintainer: Kosuke Imai <kimai@princeton.edu>
Depends: R (>= 2.1), MASS
Description: MNP is a publicly available R package that 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
software 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.
LazyLoad: yes
LazyData: yes
License: GPL (version 2 or later)
URL: http://imai.princeton.edu/research/MNP.html
Packaged: Mon Apr 24 22:02:28 2006; kimai