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 |
| 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:
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