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bayesm (version 1.1-2)

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), Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial Regression, Multivariate Mixtures of Normals, 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 Allenby, McCulloch and Rossi.

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Version

Install

install.packages('bayesm')

Monthly Downloads

10,837

Version

1.1-2

License

GPL (version 2 or later)

Maintainer

Peter Rossi

Last Published

September 24th, 2023

Functions in bayesm (1.1-2)

Compute Marginal Densities of A Normal Mixture Averaged over MCMC Draws

Bank Card Conjoint Data of Allenby and Ginter (1995)

Simulate from Multivariate Probit Model

Survey Data on Brands of Scotch Consumed

Customer Satifaction Data

MCMC Algorithm for Hierachical Binary Logit

Household Panel Data on Margarine Purchases

Evaluate Log Likelihood for non-homothetic Logit Model

Draw from Wishart and Inverted Wishart Distribution

Sliced Cheese Data

Evaluate Log Likelihood for Multinomial Probit Model

Compute Marginal Density for Multivariate Normal Mixture

Draw from the Posterior of a Multivariate Regression

Evaluate Log Likelihood for Multinomial Logit Model

init.rmultiregfp

Initialize Variables for Multivariate Regression Draw

Physician Detailing Data from Manchanda et al (2004)

Draw from Posterior for Univariate Regression

Compute Log of Inverted Chi-Squared Density

Create X Matrix for Use in Multinomial Logit and Probit Routines

rhierMnlRwMixture

MCMC Algorithm for Hierarchical Multinomial Logit with Mixture of Normals Heterogeneity

Gibbs Sampler for Linear "IV" Model

Obtain A List of Cut-offs for Scale Usage Problems

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

Gibbs Sampler for Univariate Regression

Gibbs Sampler for Multivariate Probit

Computes -Expected Hessian for Multinomial Logit

Compute Log Marginal Density Using Newton-Raftery Approx

Compute Log of Multivariate Student-t Density

Compute GHK approximation to Multivariate Normal Integrals

Compute Log of Inverted Wishart Density

Gibbs Sampler for Normal Mixtures

Draw from the Posterior of a Multivariate Regression

Draw from Mixture of Normals

Draw from Truncated Univariate Normal

Gibbs Sampler (Albert and Chib) for Binary Probit

Gibbs Sampler for Normal Mixtures w/o Error Checking

Flush Console Buffer

Draw From Dirichlet Distribution

Simulate from Multinomial Logit Model

Simulate from Multinomial Probit Model

MCMC Algorithm for Negative Binomial Regression

MCMC Algorithm for Multivariate Ordinal Data with Scale Usage Heterogeneity.

Simulate from Non-homothetic Logit Model

Draw from Multivariate Student-t

Convert Covariance Matrix to a Correlation Matrix

Compute Log of Multivariate Normal Density

Simulate from MNL given X Matrix

Compute Posterior Expectation of Normal Mixture Model Moments

Posterior Draws from a Univariate Regression with Unit Error Variance

MCMC Algorithm for Negative Binomial Regression

Compute Numerical Standard Error and Relative Numerical Efficiency

rhierLinearModel

Gibbs Sampler for Hierarchical Linear Model

Gibbs Sampler for Multinomial Probit

rmnlIndepMetrop

MCMC Algorithm for Multinomial Logit Model