Package: scalablebayesm 0.2

Federico Bumbaca

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.

Authors:Federico Bumbaca [aut, cre], Jackson Novak [aut]

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scalablebayesm/json (API)

# Install 'scalablebayesm' in R:
install.packages('scalablebayesm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.00 score 13 exports 3 dependencies

Last updated 28 days agofrom:9b4927b7d9. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 25 2025
R-4.5-linux-x86_64OKFeb 25 2025

Exports:combine_drawsdrawMixturedrawPosteriorParallelhellopartition_datarheteroLinearIndepMetroprheteroMnlIndepMetroprhierLinearDPParallelrhierLinearMixtureParallelrhierMnlDPParallelrhierMnlRwMixtureParallels_maxsample_data

Dependencies:bayesmRcppRcppArmadillo