bpcs

bpcs implements Bayesian paired-comparison models in Stan as an R package to estimate item abilities, contest outcomes, and posterior ranks from paired comparison data in behavioral research.

Key Features:

• Bayesian Framework: Uses Bayesian inference to analyze paired comparison data, providing posterior distributions for model parameters.
• Model Flexibility and Extension: Allows extension of paired comparison models to accommodate varied study designs and latent variables.
• Interpretability: Produces credible intervals and posterior summaries that support interpretation of parameter estimates.
• Error Control and Robustness: Offers approaches intended to improve control of type I error rates and to provide evidence toward null hypotheses.
• Uncertainty Propagation: Propagates uncertainty through models with many parameters or latent variables via posterior inference.
• Incorporation of Prior Information: Enables inclusion of prior information in analyses consistent with Bayesian principles.
• Performance: Operates efficiently for models with numerous parameters and latent variables through Stan-based computation.

Scientific Applications:

• Behavioral research with paired comparisons: Applied to analyze paired comparison data common in behavioral studies to estimate relative preferences or abilities.
• Alternative to frequentist approaches: Serves as a Bayesian alternative for reanalysis or comparison with frequentist models such as the Bradley–Terry model.
• Estimation of contest outcomes and ranks: Used to derive posterior distributions of contest outcomes between items and posterior ranks.

Methodology:

Implements Bayesian inference via Stan using Markov Chain Monte Carlo (MCMC) to estimate posterior distributions of all model parameters and to evaluate those posteriors for contest outcomes and posterior ranks.