Changelog

Source: NEWS.md

BayesianQDM 0.1.0

Resubmission

• Expanded the MCMC acronym to “Markov chain Monte Carlo (MCMC)” in DESCRIPTION.
• Added a reference to Kang, Yamaguchi, and Han (2026) in DESCRIPTION.
• Replaced \dontrun{} with \donttest{} in examples for getgamma2bin() and getgamma2cont().

Initial Release

Initial release providing a comprehensive Bayesian quantitative decision-making framework for clinical trials with single and two-endpoint analyses for binary and continuous outcomes.

Decision Probability Functions

• pbayesdecisionprob1bin() - Go/NoGo/Gray decision probabilities for a single binary endpoint
• pbayesdecisionprob1cont() - Go/NoGo/Gray decision probabilities for a single continuous endpoint
• pbayesdecisionprob2bin() - Go/NoGo/Gray decision probabilities for two binary endpoints
• pbayesdecisionprob2cont() - Go/NoGo/Gray decision probabilities for two continuous endpoints

S3 Methods for Decision Probability Classes

• print.pbayesdecisionprob1bin() - Print method for pbayesdecisionprob1bin objects
• print.pbayesdecisionprob1cont() - Print method for pbayesdecisionprob1cont objects
• print.pbayesdecisionprob2bin() - Print method for pbayesdecisionprob2bin objects
• print.pbayesdecisionprob2cont() - Print method for pbayesdecisionprob2cont objects
• plot.pbayesdecisionprob1bin() - Plot method for pbayesdecisionprob1bin objects; displays Go/NoGo/Gray decision probabilities as a line plot across treatment scenarios
• plot.pbayesdecisionprob1cont() - Plot method for pbayesdecisionprob1cont objects; displays Go/NoGo/Gray decision probabilities as a line plot across treatment scenarios
• plot.pbayesdecisionprob2bin() - Plot method for pbayesdecisionprob2bin objects; displays Go/NoGo/Gray decision probabilities as a tile or scatter plot over a two-dimensional treatment scenario grid
• plot.pbayesdecisionprob2cont() - Plot method for pbayesdecisionprob2cont objects; displays Go/NoGo/Gray decision probabilities as a tile or scatter plot over a two-dimensional treatment scenario grid

S3 Methods for Optimal Threshold Search Classes

• plot.getgamma1bin() - Plot method for getgamma1bin objects; displays calibration curves of marginal Go and NoGo probabilities against the threshold grid for a single binary endpoint
• plot.getgamma1cont() - Plot method for getgamma1cont objects; displays calibration curves of marginal Go and NoGo probabilities against the threshold grid for a single continuous endpoint
• plot.getgamma2bin() - Plot method for getgamma2bin objects; displays calibration curves of marginal Go and NoGo probabilities against the threshold grid for two binary endpoints
• plot.getgamma2cont() - Plot method for getgamma2cont objects; displays calibration curves of marginal Go and NoGo probabilities against the threshold grid for two continuous endpoints

Posterior and Predictive Probability Functions

• pbayespostpred1bin() - Posterior or predictive probability for a single binary endpoint
• pbayespostpred1cont() - Posterior or predictive probability for a single continuous endpoint
• pbayespostpred2bin() - Joint region probabilities for two binary endpoints
• pbayespostpred2cont() - Joint region probabilities for two continuous endpoints

Optimal Threshold Search Functions

• getgamma1bin() - Find optimal Go/NoGo thresholds for a single binary endpoint by grid search over candidate gamma values
• getgamma1cont() - Find optimal Go/NoGo thresholds for a single continuous endpoint by grid search over candidate gamma values
• getgamma2bin() - Find optimal Go/NoGo thresholds for two binary endpoints by grid search over candidate gamma value pairs
• getgamma2cont() - Find optimal Go/NoGo thresholds for two continuous endpoints by grid search over candidate gamma value pairs

Distribution Functions for Continuous Endpoints

• ptdiff_NI() - CDF for the difference of two t-distributions via numerical integration
• ptdiff_MC() - CDF for the difference of two t-distributions via Monte Carlo simulation
• ptdiff_MM() - CDF for the difference of two t-distributions via Moment-Matching approximation

Distribution Functions for Binary Endpoints

• pbetadiff() - CDF for the difference of two independent Beta distributions
• pbetabinomdiff() - Beta-binomial posterior predictive probability

Sampling Functions

• rdirichlet() - Random sampler for the Dirichlet distribution

Utility Functions

• getjointbin() - Joint binary probability from marginals and a correlation parameter
• allmultinom() - Enumerate all multinomial outcome combinations

Study Designs

• Controlled design
• Uncontrolled design (hypothetical control)
• External design with power priors

Prior Distributions

• Binary endpoints: Beta and Dirichlet conjugate priors
• Single continuous endpoints: Normal-Inverse-Chi-squared conjugate prior and vague (Jeffreys) prior
• Two continuous endpoints: Normal-Inverse-Wishart conjugate prior and vague prior

Calculation Methods for Continuous Endpoints

• NI (Numerical Integration): exact computation via adaptive quadrature
• MC (Monte Carlo): simulation-based estimation
• MM (Moment-Matching): closed-form approximation; fully vectorised and recommended for large-scale simulation studies