| bayprior-package | bayprior: Bayesian Prior Elicitation for Clinical Trials |
| aggregate_experts | Aggregate multiple expert priors into a consensus prior |
| as_prior | Constructor for bayprior from raw parameters |
| bayprior | bayprior: Bayesian Prior Elicitation for Clinical Trials |
| calibrate_power_prior | Calibrate power prior weight via Bayes Factor |
| conflict_mahalanobis | Multivariate prior-data conflict via Mahalanobis distance |
| elicit_beta | Elicit a Beta prior via quantile matching or moment matching |
| elicit_exponential | Elicit an Exponential prior via moments, rate, or quantile matching |
| elicit_gamma | Elicit a Gamma prior via quantile matching or moment matching |
| elicit_lognormal | Elicit a Log-Normal prior via quantile matching or moment matching |
| elicit_mixture | Elicit a mixture prior |
| elicit_normal | Elicit a Normal prior via quantile matching or moment matching |
| elicit_roulette | Roulette-method elicitation (chip-allocation) |
| elicit_weibull | Elicit a Weibull prior via moments, direct parameters, or quantile matching |
| plot.bayprior_power_prior | Plot calibration curve for power prior weight selection |
| plot_prior_likelihood | Plot prior, likelihood, and posterior density overlays |
| plot_sensitivity | Plot sensitivity analysis results |
| plot_tornado | Tornado plot of prior influence on posterior quantities |
| print.bayprior | Print method for bayprior objects |
| print.bayprior_conflict | Print method for bayprior_conflict objects |
| print.bayprior_conflict_mv | Print method for multivariate conflict objects |
| print.bayprior_power_prior | Print method for bayprior_power_prior objects |
| prior_conflict | Compute prior-data conflict diagnostics |
| prior_report | Generate a Prior Justification Report |
| robust_prior | Construct a robust (heavy-tailed mixture) prior |
| run_app | Run the bayprior Shiny Application |
| sceptical_prior | Construct a sceptical (penalised-enthusiasm) prior |
| sensitivity_cri | Sensitivity of posterior CrI to prior hyperparameters |
| sensitivity_grid | Sensitivity grid over prior hyperparameters |
| summary.bayprior | Summary method for bayprior objects |