bayesm3.1-6 package

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).

Maintainer: Peter Rossi License: GPL (>= 2) Last published: 2023-09-23