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scalablebayesm (version 0.2)

Distributed Markov Chain Monte Carlo for Bayesian Inference in Marketing

Description

Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes: 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. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

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Version

Install

install.packages('scalablebayesm')

Monthly Downloads

137

Version

0.2

License

GPL (>= 2)

Maintainer

Federico Bumbaca

Last Published

February 25th, 2025

Functions in scalablebayesm (0.2)

partition_data

Partition Data Into Shards
rheteroLinearIndepMetrop

Distributed Independence Metropolis-Hastings Algorithm for Draws From Multivariate Normal Distribution
rhierLinearMixtureParallel

MCMC Algorithm for Hierarchical Multinomial Linear Model with Mixture-of-Normals Heterogeneity
hello

A placeholder function using roxygen
rhierMnlDPParallel

MCMC Algorithm for Hierarchical Multinomial Logit with Dirichlet Process Prior Heterogeneity
drawMixture

Gibbs Sampler Inference for a Mixture of Multivariate Normals
drawPosteriorParallel

Draw from Posterior Parallel Distribution
rhierLinearDPParallel

MCMC Algorithm for Hierarchical Linear Model with Dirichlet Process Prior Heterogeneity
combine_draws

Combine Lists of Draws From a Posterior Predictive Distribution
rheteroMnlIndepMetrop

Independence Metropolis-Hastings Algorithm for Draws From Multinomial Distribution
sample_data

Sample Data
rhierMnlRwMixtureParallel

MCMC Algorithm for Hierarchical Multinomial Logit with Mixture-of-Normals Heterogeneity
s_max

Calculate Maximum Number of Shards