Package: bayprior 0.5.0

bayprior: Bayesian Prior Elicitation, Diagnostics, and Regulatory Reporting

A toolkit for constructing, validating, and justifying Bayesian priors in clinical trial settings. Implements expert elicitation via quantile matching, the roulette method, and moment matching across six distribution families, linear and logarithmic expert pooling, meta-analytic-predictive (MAP) priors derived from historical trials, prior-data conflict diagnostics including the Box p-value (computed from the exact prior predictive distribution where one has a closed form), an exploratory surprisal (S-value) companion to it, surprise index, information divergence, and Mahalanobis distance, sensitivity analyses with tornado and influence heatmap plots, sceptical, robust, and power priors, and automated prior justification reports. Includes a fully modular 'Shiny' application for interactive use. Methods based on O'Hagan et al. (2006, ISBN:9780470029886), Box (1980) <doi:10.2307/2982063>, Greenland (2023) <doi:10.1111/sjos.12625>, Oakley and O'Hagan (2010) <https://tonyohagan.co.uk/shelf/>, Schmidli et al. (2014) <doi:10.1111/biom.12242>, Neuenschwander et al. (2010) <doi:10.1177/1740774509356002>, Roever et al. (2021) <doi:10.1002/jrsm.1475>, Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, Spiegelhalter et al. (1994) <doi:10.2307/2983527>.

Authors:Ndoh Penn [aut, cre]

bayprior_0.5.0.tar.gz
bayprior_0.5.0.zip(r-4.7-any)bayprior_0.5.0.zip(r-4.6-any)bayprior_0.5.0.zip(r-4.5-any)
bayprior_0.5.0.tgz(r-4.6-any)bayprior_0.5.0.tgz(r-4.5-any)
bayprior_0.5.0.tar.gz(r-4.7-any)bayprior_0.5.0.tar.gz(r-4.6-any)
bayprior_0.5.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bayprior/json (API)

# Install 'bayprior' in R:
install.packages('bayprior', repos = c('https://ndohpenngit.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ndohpenngit/bayprior/issues

On CRAN:

Conda:

bayesianclinical-trialspharmareportingshiny

6.05 score 2 stars 485 downloads 26 exports 78 dependencies

Last updated from:6db9cf5f95. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK222
source / vignettesOK581
linux-releaseOK206
macos-releaseOK145
macos-oldrelOK149
windows-develOK163
windows-releaseOK150
windows-oldrelOK145
wasm-releaseOK151

Exports:aggregate_expertsas_priorcalibrate_power_priorconflict_mahalanobiselicit_betaelicit_exponentialelicit_gammaelicit_lognormalelicit_mixtureelicit_normalelicit_rouletteelicit_weibullhistorical_effect_sizesmap_priorplot_prior_likelihoodplot_sensitivityplot_tau_posteriorplot_tornadoprior_conflictprior_reportresolve_tau_priorrobust_priorrun_appsceptical_priorsensitivity_crisensitivity_grid

Dependencies:askpassattemptbase64encbslibcachemclicodetoolscommonmarkconfigcpp11crosstalkcurldata.tabledigestdplyrDTevaluatefarverfastmapfontawesomefsgenericsggplot2gluegolemgtablehighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesshinyshinycssloadersshinydashboardshinyjsshinyWidgetssourcetoolsstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

Prior-Data Conflict Diagnostics
Why Conflict Diagnostics Matter | Supported Data Types | Univariate Conflict: prior_conflict() | Binary Data | The Diagnostics Explained | 1. Box's Prior Predictive P-value | Exact prior predictive version | 2. S-value (Surprisal) | 3. Surprise Index | 4. KL Divergence | 5. Bhattacharyya Overlap Coefficient | Threshold Summary | Prior-Likelihood-Posterior Overlay | Continuous Endpoint Conflict | Poisson / Count Data Conflict | Survival / Time-to-Event Data Conflict | Compatibility Validation | Mixture Prior Conflict | Multivariate Conflict: conflict_mahalanobis() | Conflict Severity Classification | References
Rendered fromconflict-diagnostics.Rmdusingknitr::rmarkdown

Last update: 2026-10-17
Started: 2026-04-30

Regulatory Reporting
Regulatory Context | The Prior Justification Report | What It Contains | Output Formats | Generating a Report | The Notes Field | The FDA/EMA Compliance Checklist | Session Reproducibility | Working with the Shiny App | Pre-Submission Checklist | References
Rendered fromregulatory-reporting.Rmdusingknitr::rmarkdown

Last update: 2026-10-09
Started: 2026-04-30

Using bayprior Priors with rstanarm and brms
Overview | Deriving the prior | Handoff to rstanarm | Handoff to brms | Summary
Rendered fromusing-with-rstanarm-brms.Rmdusingknitr::rmarkdown

Last update: 2026-10-07
Started: 2026-10-07

Introduction to bayprior
Overview | Prior Elicitation | Beta Prior (Binary Endpoints) | Quantile Matching | Moment Matching | Normal Prior (Continuous Endpoints) | Gamma Prior (Rate / Count Endpoints) | Log-Normal Prior (Hazard Ratios) | Roulette Method | Expert Pooling | Linear Pooling | Manual Mixture Construction | Meta-Analytic-Predictive (MAP) Priors | Prior-Data Conflict Diagnostics | Sensitivity Analysis | Tornado Plot | Influence Heatmap | Credible Interval Sensitivity | Robust and Sceptical Priors | Robust Mixture Prior | Sceptical Prior | Power Prior Calibration | Multivariate Conflict Diagnostics | Generating a Prior Justification Report | Complete Worked Example | Function Reference | References
Rendered frombayprior-introduction.Rmdusingknitr::rmarkdown

Last update: 2026-09-22
Started: 2026-04-29

Robust, Sceptical, and Power Priors
Overview | Robust Mixture Prior | Concept | Usage | Varying the Vague Weight | Vague SD Multiplier | Deriving the Informative Prior from Historical Trials (MAP) | Getting y and se from Raw Trial Data | Choosing the Heterogeneity Prior Scale | Inspecting the Heterogeneity Posterior | Sceptical Prior | Normal Family (Mean Differences, Log Odds Ratios) | Beta Family (Response Rates) | Log-Normal Family (Hazard Ratios) | Enthusiastic vs Sceptical Pair | Power Prior | Calibrating $\delta$ | Compatibility Method | Power Prior for Other Families | Choosing the Right Alternative Prior | Including Robust Priors in the Regulatory Report | References
Rendered fromrobust-priors.Rmdusingknitr::rmarkdown

Last update: 2026-09-22
Started: 2026-04-30

Sensitivity Analysis
Why Sensitivity Analysis Is Required | Supported Data Types | Binary Data | Credible Interval Sensitivity | Poisson / Count Data | Survival / Time-to-Event Data | Continuous Endpoints | Interpreting Influence Scores | Mixture Prior Sensitivity | Regulatory Reporting | References
Rendered fromsensitivity-analysis.Rmdusingknitr::rmarkdown

Last update: 2026-08-05
Started: 2026-04-30

Prior Elicitation Methods
Overview | Distribution Families | Elicitation Methods | Beta Prior (Binary Endpoints) | Quantile Matching | Moment Matching | Normal Prior (Continuous Endpoints) | Gamma Prior (Rate and Count Endpoints) | Log-Normal Prior (Hazard Ratios) | Exponential Prior (Constant Hazard Rates) | Method: moments | Method: rate | Method: quantile | Conjugate update | Weibull Prior (Non-Constant Hazard Survival Times) | Method: params | Posterior approximation note | Roulette Method | Expert Pooling | Linear Pooling | Pooling Compatibility | Prior Object Structure | References
Rendered fromprior-elicitation.Rmdusingknitr::rmarkdown

Last update: 2026-05-15
Started: 2026-04-30

Readme and manuals

Help Manual

Help pageTopics
Aggregate multiple expert priors into a consensus prioraggregate_experts
Constructor for bayprior from raw parametersas_prior
bayprior: Bayesian Prior Elicitation, Diagnostics, and Regulatory Reportingbayprior-package bayprior
Calibrate power prior weight via Bayes Factorcalibrate_power_prior
Multivariate prior-data conflict via Mahalanobis distanceconflict_mahalanobis
Elicit a Beta prior via quantile matching or moment matchingelicit_beta
Elicit an Exponential prior via moments, rate, or quantile matchingelicit_exponential
Elicit a Gamma prior via quantile matching or moment matchingelicit_gamma
Elicit a Log-Normal prior via quantile matching or moment matchingelicit_lognormal
Elicit a mixture priorelicit_mixture
Elicit a Normal prior via quantile matching or moment matchingelicit_normal
Roulette-method elicitation (chip-allocation)elicit_roulette
Elicit a Weibull prior via moments, direct parameters, or quantile matchingelicit_weibull
Compute (y, se) inputs for map_prior() from raw historical-trial datahistorical_effect_sizes
Derive a meta-analytic-predictive (MAP) prior from historical trialsmap_prior
Plot prior, likelihood, and posterior density overlaysplot_prior_likelihood
Plot sensitivity analysis resultsplot_sensitivity
Plot the posterior distribution of the heterogeneity parameter (tau)plot_tau_posterior
Tornado plot of prior influence on posterior quantitiesplot_tornado
Plot calibration curve for power prior weight selectionplot.bayprior_power_prior
Print method for bayprior objectsprint.bayprior
Print method for bayprior_conflict objectsprint.bayprior_conflict
Print method for multivariate conflict objectsprint.bayprior_conflict_mv
Print method for bayprior_power_prior objectsprint.bayprior_power_prior
Compute prior-data conflict diagnosticsprior_conflict
Generate a Prior Justification Reportprior_report
Resolve the default heterogeneity (tau) prior for an outcome typeresolve_tau_prior
Construct a robust (heavy-tailed mixture) priorrobust_prior
Run the bayprior Shiny Applicationrun_app
Construct a sceptical (penalised-enthusiasm) priorsceptical_prior
Credible interval sensitivity over prior hyperparameterssensitivity_cri
Sensitivity grid over prior hyperparameterssensitivity_grid
Summary method for bayprior objectssummary.bayprior