Bayesian Inference for Marketing/Micro-Econometrics
Posterior Draws from a Univariate Regression with Unit Error Variance
Obtain A List of Cut-offs for Scale Usage Problems
Cluster Observations Based on Indicator MCMC Draws
Computes Conditional Mean/Var of One Element of MVN given All Others
Create X Matrix for Use in Multinomial Logit and Probit Routines
Compute Marginal Densities of A Normal Mixture Averaged over MCMC Draw...
Compute GHK approximation to Multivariate Normal Integrals
Evaluate Log Likelihood for Multinomial Logit Model
Evaluate Log Likelihood for Multinomial Probit Model
Evaluate Log Likelihood for non-homothetic Logit Model
Compute Log of Inverted Chi-Squared Density
Compute Log of Inverted Wishart Density
Compute Log of Multivariate Normal Density
Compute Log of Multivariate Student-t Density
Compute Log Marginal Density Using Newton-Raftery Approx
Compute Marginal Density for Multivariate Normal Mixture
Compute Bivariate Marginal Density for a Normal Mixture
Computes --Expected Hessian for Multinomial Logit
Compute MNP Probabilities
Compute Posterior Expectation of Normal Mixture Model Moments
Convert Covariance Matrix to a Correlation Matrix
Compute Numerical Standard Error and Relative Numerical Efficiency
Plot Method for Hierarchical Model Coefs
Plot Method for Arrays of MCMC Draws
Plot Method for MCMC Draws of Normal Mixtures
Bayesian Analysis of Random Coefficient Logit Models Using Aggregate D...
Illustrate Bivariate Normal Gibbs Sampler
Gibbs Sampler (Albert and Chib) for Binary Probit
Draw From Dirichlet Distribution
Density Estimation with Dirichlet Process Prior and Normal Base
MCMC Algorithm for Hierarchical Binary Logit
Gibbs Sampler for Hierarchical Linear Model with Mixture-of-Normals He...
Gibbs Sampler for Hierarchical Linear Model with Normal Heterogeneity
MCMC Algorithm for Hierarchical Multinomial Logit with Dirichlet Proce...
MCMC Algorithm for Hierarchical Multinomial Logit with Mixture-of-Norm...
MCMC Algorithm for Hierarchical Negative Binomial Regression
Linear "IV" Model with DP Process Prior for Errors
Gibbs Sampler for Linear "IV" Model
Gibbs Sampler for Normal Mixtures w/o Error Checking
Draw from Mixture of Normals
MCMC Algorithm for Multinomial Logit Model
Gibbs Sampler for Multinomial Probit
Draw from the Posterior of a Multivariate Regression
Gibbs Sampler for Multivariate Probit
Draw from Multivariate Student-t
MCMC Algorithm for Negative Binomial Regression
Gibbs Sampler for Normal Mixtures
Gibbs Sampler for Ordered Probit
MCMC Algorithm for Multivariate Ordinal Data with Scale Usage Heteroge...
Gibbs Sampler for Seemingly Unrelated Regressions (SUR)
Draw from Truncated Univariate Normal
IID Sampler for Univariate Regression
Gibbs Sampler for Univariate Regression
Draw from Wishart and Inverted Wishart Distribution
Simulate from Non-homothetic Logit Model
Summarize Mcmc Parameter Draws
Summarize Draws of Normal Mixture Components
Summarize Draws of Var-Cov Matrices
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 first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).