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, Analysis
of Multivariate Ordinal survey data with scale usage heterogeneity (as
in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random
Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian
Non- and Semi-Parametric Methods and Applications (Princeton U Press
2014).

Homepage:
https://CRAN.R-project.org/package=bayesm
