scalablebayesm: Distributed Markov Chain Monte Carlo for Bayesian Inference in Marketing

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) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.

Version: 0.2
Imports: Rcpp (≥ 1.0.9), parallel, bayesm
LinkingTo: Rcpp, RcppArmadillo, bayesm
Published: 2025-02-25
DOI: 10.32614/CRAN.package.scalablebayesm
Author: Federico Bumbaca [aut, cre], Jackson Novak [aut]
Maintainer: Federico Bumbaca <federico.bumbaca at colorado.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: scalablebayesm results

Documentation:

Reference manual: scalablebayesm.pdf

Downloads:

Package source: scalablebayesm_0.2.tar.gz
Windows binaries: r-devel: scalablebayesm_0.2.zip, r-release: not available, r-oldrel: scalablebayesm_0.2.zip
macOS binaries: r-devel (arm64): scalablebayesm_0.2.tgz, r-release (arm64): scalablebayesm_0.2.tgz, r-oldrel (arm64): scalablebayesm_0.2.tgz, r-devel (x86_64): scalablebayesm_0.2.tgz, r-release (x86_64): scalablebayesm_0.2.tgz, r-oldrel (x86_64): scalablebayesm_0.2.tgz

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