Package: bayesm
Version: 2.2-4
Date: 2011-02-25
Title: Bayesian Inference for Marketing/Micro-econometrics
Author: Peter Rossi <perossichi@gmail.com>.
Maintainer: Peter Rossi <perossichi@gmail.com>
Depends: R (>= 2.12.1)
Description: bayesm covers many important models used in marketing and
micro-econometrics applications. The package includes: Bayes
Regression (univariate or multivariate dep var), Bayes
Seemingly Unrelated Regression (SUR), Binary and Ordinal
Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit, Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and
covariates, Hierarchical Multinomial Logits with a mixture of
normals prior and covariates, Hierarchical Multinomial Logits
with a Dirichlet Process prior and covariates, Hierarchical
Negative Binomial Regression Models, Bayesian analysis of
choice-based conjoint data, Bayesian treatment of linear
instrumental variables models, and Analysis of Multivariate
Ordinal survey data with scale usage heterogeneity (as in Rossi
et al, JASA (01)). For further reference, consult our book,
Bayesian Statistics and Marketing by Rossi, Allenby and
McCulloch.
License: GPL (>= 2)
URL: http://www.perossi.org/home/bsm-1
Packaged: 2011-02-26 20:38:56 UTC; perossichi
Repository: CRAN
Date/Publication: 2011-02-27 18:05:01

Package: bayesm
Version: 2.2-5
Date: 2012-05-15
Title: Bayesian Inference for Marketing/Micro-econometrics
Author: Peter Rossi <perossichi@gmail.com>.
Maintainer: Peter Rossi <perossichi@gmail.com>
Depends: R (>= 2.10)
Description: bayesm covers many important models used in marketing and
micro-econometrics applications.  The package includes: Bayes
Regression (univariate or multivariate dep var), Bayes
Seemingly Unrelated Regression (SUR), Binary and Ordinal
Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP),
Multivariate Probit, Negative Binomial (Poisson) Regression,
Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and
covariates, Hierarchical Multinomial Logits with a mixture of
normals prior and covariates, Hierarchical Multinomial Logits
with a Dirichlet Process prior and covariates, Hierarchical
Negative Binomial Regression Models, Bayesian analysis of
choice-based conjoint data, Bayesian treatment of linear
instrumental variables models, and Analysis of Multivariate
Ordinal survey data with scale usage heterogeneity (as in Rossi
et al, JASA (01)).  For further reference, consult our book,
Bayesian Statistics and Marketing by Rossi, Allenby and
McCulloch.
License: GPL (>= 2)
URL: http://www.perossi.org/home/bsm-1
Packaged: 2012-05-15 22:08:17 UTC; perossichi
Repository: CRAN
Date/Publication: 2012-05-16 07:49:34