The bayesm package provides implementations for Bayesian inference models commonly used in marketing and micro-econometrics. It includes models such as regression, multinomial logit, multivariate probit, hierarchical models, and analysis of conjoint data. The package is authored by Peter Rossi and is referenced in the book 'Bayesian Statistics and Marketing'.
Use Cases
- Estimating consumer choice probabilities based on multinomial logit and probit models.
- Analyzing survey data with scale usage heterogeneity using multivariate ordinal models.
- Performing hierarchical regression analysis with mixture of normals priors.
- Conducting Bayesian analysis of linear instrumental variables models.
- Modeling aggregate market demand using random coefficient logit models.
Strengths
- The description explicitly lists over 15 specific Bayesian models.
- The package is directly referenced in authoritative textbooks on Bayesian statistics and marketing.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Peter Rossi