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bayesm (version 2.0-5)

Bayesian Inference for Marketing/Micro-econometrics

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), Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Hierarchical Linear Models with normal prior and covariates, Hierarchical Multinomial Logits with mixture of normals prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, and Analyis 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.

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Version

Install

install.packages('bayesm')

Monthly Downloads

11,239

Version

2.0-5

License

GPL (version 2 or later)

Maintainer

Peter Rossi

Last Published

September 24th, 2023

Functions in bayesm (2.0-5)

Survey Data on Brands of Scotch Consumed

MCMC Algorithm for Hierarchical Binary Logit

Illustrate Bivariate Normal Gibbs Sampler

Sliced Cheese Data

Create X Matrix for Use in Multinomial Logit and Probit Routines

Flush Console Buffer

Computes Conditional Mean/Var of One Element of MVN given All Others

Compute GHK approximation to Multivariate Normal Integrals

MCMC Algorithm for Negative Binomial Regression

Cluster Observations Based on Indicator MCMC Draws

Compute Marginal Densities of A Normal Mixture Averaged over MCMC Draws

Obtain A List of Cut-offs for Scale Usage Problems

Compute Posterior Expectation of Normal Mixture Model Moments

init.rmultiregfp

Initialize Variables for Multivariate Regression Draw

Physician Detailing Data from Manchanda et al (2004)

Compute Marginal Density for Multivariate Normal Mixture

Draw from Mixture of Normals

Compute Log Marginal Density Using Newton-Raftery Approx

Gibbs Sampler for Multivariate Probit

Draw from the Posterior of a Multivariate Regression

Gibbs Sampler for Seemingly Unrelated Regressions (SUR)

MCMC Algorithm for Negative Binomial Regression

IID Sampler for Univariate Regression

Evaluate Log Likelihood for non-homothetic Logit Model

MCMC Algorithm for Multivariate Ordinal Data with Scale Usage Heterogeneity.

Household Panel Data on Margarine Purchases

Simulate from Multinomial Probit Model

Simulate from Multivariate Probit Model

Draw from Multivariate Student-t

rhierLinearModel

Gibbs Sampler for Hierarchical Linear Model

Compute Bivariate Marginal Density for a Normal Mixture

Simulate from Multinomial Logit Model

Evaluate Log Likelihood for Multinomial Probit Model

Gibbs Sampler (Albert and Chib) for Binary Probit

Gibbs Sampler for Multinomial Probit

Compute Log of Multivariate Student-t Density

Gibbs Sampler for Linear "IV" Model

Posterior Draws from a Univariate Regression with Unit Error Variance

Simulate from MNL given X Matrix

Compute Log of Inverted Chi-Squared Density

rmnlIndepMetrop

MCMC Algorithm for Multinomial Logit Model

Draw From Dirichlet Distribution

Compute Numerical Standard Error and Relative Numerical Efficiency

Draw from Truncated Univariate Normal

Gibbs Sampler for Normal Mixtures

Simulate from Non-homothetic Logit Model

Compute Log of Inverted Wishart Density

Draw from the Posterior of a Multivariate Regression

Gibbs Sampler for Univariate Regression

Customer Satifaction Data

Draw from Wishart and Inverted Wishart Distribution

Gibbs Sampler for Normal Mixtures w/o Error Checking

rhierMnlRwMixture

MCMC Algorithm for Hierarchical Multinomial Logit with Mixture of Normals Heterogeneity

Bank Card Conjoint Data of Allenby and Ginter (1995)

Evaluate Log Likelihood for Multinomial Logit Model

Computes -Expected Hessian for Multinomial Logit

Compute Log of Multivariate Normal Density

Convert Covariance Matrix to a Correlation Matrix

Compute MNP Probabilities