Package index • RBesT

Package overview

Combine historical data into a Meta-Analytic-Predictive (MAP) prior and inspect the model fit.

gMAP() print(<gMAP>) fitted(<gMAP>) coef(<gMAP>) as.matrix(<gMAP>) summary(<gMAP>): Meta-Analytic-Predictive Analysis for Generalized Linear Models
predict(<gMAP>) print(<gMAPpred>) summary(<gMAPpred>) as.matrix(<gMAPpred>): Predictions from gMAP analyses
plot(<gMAP>): Diagnostic plots for gMAP analyses
forest_plot(): Forest Plot
as_draws(<gMAP>) as_draws_matrix(<gMAP>) as_draws_array(<gMAP>) as_draws_df(<gMAP>) as_draws_list(<gMAP>) as_draws_rvars(<gMAP>): Transform gMAP to draws objects
nsamples(<gMAP>): Return the number of posterior samples

Approximate MCMC samples (or any sample) by a parametric mixture of conjugate distributions.

mixfit(): Fit of Mixture Densities to Samples
automixfit(): Automatic Fitting of Mixtures of Conjugate Distributions to a Sample
plot(<EM>): Diagnostic plots for EM fits

Quantify the informativeness of a (mixture) prior.

Make a prior robust against prior-data conflict.

Define decision rules and evaluate operating characteristics and the probability of success for 1- and 2-sample designs.

decision1S() has_lower() has_upper() lower() upper() oc1Sdecision(): Decision Function for 1 Sample Designs
decision2S() oc2Sdecision(): Decision Function for 2 Sample Designs
decision1S_boundary(): Decision Boundary for 1 Sample Designs
decision2S_boundary(): Decision Boundary for 2 Sample Designs
oc1S(): Operating Characteristics for 1 Sample Design
oc2S(): Operating Characteristics for 2 Sample Design
pos1S(): Probability of Success for a 1 Sample Design
pos2S(): Probability of Success for 2 Sample Design

Conjugate posterior updating and predictive distributions used to analyse the actual trial outcome.

Construct, combine and evaluate mixture distributions.

mixbeta() ms2beta() mn2beta() print(<betaMix>) print(<betaBinomialMix>) summary(<betaMix>) summary(<betaBinomialMix>): Beta Mixture Density
mixgamma() ms2gamma() mn2gamma() print(<gammaMix>) print(<gammaPoissonMix>) print(<gammaExpMix>) summary(<gammaMix>) summary(<gammaPoissonMix>): The Gamma Mixture Distribution
mixnorm() mn2norm() print(<normMix>) summary(<normMix>) sigma(<normMix>) `sigma<-`(): Normal Mixture Density
mixmvnorm() msr2mvnorm() print(<mvnormMix>) summary(<mvnormMix>) sigma(<mvnormMix>): Multivariate Normal Mixture Density
mixcombine(): Combine Mixture Distributions
dmix() pmix() qmix() rmix() `[[`(<mix>): Mixture Distributions
dmixdiff() pmixdiff() qmixdiff() rmixdiff(): Difference of mixture distributions
plot(<mix>) plot(<mvnormMix>): Plot mixture distributions
likelihood() `likelihood<-`(): Read and Set Likelihood to the Corresponding Conjugate Prior

Read/write mixtures, use them in brms, and helper functions.

mixstanvar(): Mixture distributions as brms priors
write_mix_json() read_mix_json() experimental: Write and Read a Mixture Object with JSON
logit() inv_logit(): Logit (log-odds) and inverse-logit function.
BinaryExactCI(): Exact Confidence interval for Binary Proportion