The robust mixture prior for a binary endpoint, with the
binomial likelihoods of both arms and an exact binomial informative
component; see the comment at the top of R/binomial_rmp.R. It replaced
a truncated normal mixture whose informative component was the normal
approximation of the source posterior.
Super classes
Model -> MCMCModel -> BinomialLatticePrior -> BinomialRMP
Methods
Inherited methods
Model$calibrate_for_design()Model$check_data()Model$create()Model$empirical_bayes_update()Model$estimate_bayesian_operating_characteristics()Model$estimate_frequentist_operating_characteristics()Model$hypothesis_space_transformation()Model$inference_cache_scope()Model$plot_pdfs()Model$plot_posterior_pdf()Model$plot_prior_pdf()Model$posterior_beta_mixture()Model$posterior_mean()Model$posterior_moments()Model$posterior_quantile()Model$posterior_to_RBesT()Model$print_model_summary()Model$prior_to_RBesT()Model$simulation_for_given_treatment_effect()Model$test_decision()Model$vectorised_replicate_inference()MCMCModel$check_mcmc_config()MCMCModel$credible_interval()MCMCModel$draw_mcmc_prior()MCMCModel$inference()MCMCModel$posterior_cdf()MCMCModel$posterior_ess()MCMCModel$posterior_median()MCMCModel$posterior_pdf()MCMCModel$prepare_data()MCMCModel$prior_cdf()MCMCModel$prior_pdf()MCMCModel$sample_posterior()MCMCModel$sample_prior()MCMCModel$stan_sampler()MCMCModel$uses_quadrature()BinomialLatticePrior$prior_elir_ess()BinomialLatticePrior$prior_given_control_rate()BinomialLatticePrior$quadrature_posterior()BinomialLatticePrior$quadrature_prior()BinomialLatticePrior$source_counts()
BinomialRMP$components()
The two components, computed once per worker and shared.
Returns
The output of binomial_rmp_components().
BinomialRMP$kernels()
The prior kernels at the current weight, kept until the weight changes.
Returns
The output of binomial_rmp_kernels().
BinomialRMP$compute_posterior_parameters()
Record the posterior weight of the informative component.