Package index

All functions

AS

Ankylosing Spondylitis.

BinaryExactCI()

Exact Confidence interval for Binary Proportion

RBesT RBesT-package

R Bayesian Evidence Synthesis Tools

automixfit()

Automatic Fitting of Mixtures of Conjugate Distributions to a Sample

colitis

Ulcerative Colitis.

crohn

Crohn's disease.

decision1S() oc1Sdecision()

Decision Function for 1 Sample Designs

decision1S_boundary()

Decision Boundary for 1 Sample Designs

decision2S() oc2Sdecision()

Decision Function for 2 Sample Designs

decision2S_boundary()

Decision Boundary for 2 Sample Designs

ess()

Effective Sample Size for a Conjugate Prior

forest_plot()

Forest Plot

gMAP() print(<gMAP>) fitted(<gMAP>) coef(<gMAP>) as.matrix(<gMAP>) summary(<gMAP>)

Meta-Analytic-Predictive Analysis for Generalized Linear Models

likelihood() `likelihood<-`()

Read and Set Likelihood to the Corresponding Conjugate Prior

logit() inv_logit()

Logit (log-odds) and inverse-logit function.

dmix() pmix() qmix() rmix() `[[`(<mix>)

Mixture Distributions

mixbeta() ms2beta() mn2beta() print(<betaMix>) print(<betaBinomialMix>) summary(<betaMix>) summary(<betaBinomialMix>)

Beta Mixture Density

mixcombine()

Combine Mixture Distributions

dmixdiff() pmixdiff() qmixdiff() rmixdiff()

Difference of mixture distributions

mixfit()

Fit of Mixture Densities to Samples

mixgamma() ms2gamma() mn2gamma() print(<gammaMix>) print(<gammaPoissonMix>) print(<gammaExpMix>) summary(<gammaMix>) summary(<gammaPoissonMix>)

The Gamma Mixture Distribution

write_mix_json() read_mix_json() experimental

Write and Read a Mixture Object with JSON

mixmvnorm() msr2mvnorm() print(<mvnormMix>) summary(<mvnormMix>) sigma(<mvnormMix>)

Multivariate Normal Mixture Density

mixnorm() mn2norm() print(<normMix>) summary(<normMix>) sigma(<normMix>) `sigma<-`()

Normal Mixture Density

plot(<mix>) plot(<mvnormMix>)

Plot mixture distributions

mixstanvar()

Mixture distributions as brms priors

oc1S()

Operating Characteristics for 1 Sample Design

oc2S()

Operating Characteristics for 2 Sample Design

plot(<EM>)

Diagnostic plots for EM fits

plot(<gMAP>)

Diagnostic plots for gMAP analyses

pos1S()

Probability of Success for a 1 Sample Design

pos2S()

Probability of Success for 2 Sample Design

postmix()

Conjugate Posterior Analysis

preddist()

Predictive Distributions for Mixture Distributions

predict(<gMAP>) print(<gMAPpred>) summary(<gMAPpred>) as.matrix(<gMAPpred>)

Predictions from gMAP analyses

robustify()

Robustify Mixture Priors

transplant

Transplant.