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RBesT (version 1.8-2)

R Bayesian Evidence Synthesis Tools

Description

Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases). Please refer to Weber et al. (2021) for details on applying this package while Neuenschwander et al. (2010) and Schmidli et al. (2014) explain details on the methodology.

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Version

Install

install.packages('RBesT')

Monthly Downloads

1,346

Version

1.8-2

License

GPL (>= 3)

Maintainer

Sebastian Weber

Last Published

April 25th, 2025

Functions in RBesT (1.8-2)

Fill numeric objects

Decision Function for 2 Sample Designs

decision1S_boundary

Decision Boundary for 1 Sample Designs

Effective Sample Size for a Conjugate Prior

decision2S_boundary

Decision Boundary for 2 Sample Designs

Meta-Analytic-Predictive Analysis for Generalized Linear Models

Transform Densities with a link function

Beta-Binomial Probabilities

Decision Function for 1 Sample Designs

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

Numerically stable log of the inv_logit function

Extract log likelihood from fitted EM objects

integrate_density_log

internal function used for integration of densities which appears to be much more stable from -Inf to +Inf in the logit space while the density to be integrated recieves inputs from 0 to 1 such that the inverse distribution function must be used. The integral solved is int_x dmix(mix,x) integrand(x) where integrand must be given as log and we integrate over the support of mix.

Combine Mixture Distributions

Read and Set Likelihood to the Corresponding Conjugate Prior

Difference of mixture distributions

Mixture Distributions

Beta Mixture Density

k nearest neighbor algorithm for multi-variate data

Write and Read a Mixture Object with JSON

The Gamma Mixture Distribution

Plot mixture distributions

Normal Mixture Density

Mixture distributions as brms priors

Utility function to instantiate 2 parameter mixture densities.

Fit of Mixture Densities to Samples

takes x and transforms it according to the defined link function of the mixture

Multivariate Normal Mixture Density

Operating Characteristics for 1 Sample Design

Predictive Distributions for Mixture Distributions

Predictions from gMAP analyses

Probability of Success for 2 Sample Design

Conjugate Posterior Analysis

Support of Distributions

Robustify Mixture Priors

Find root of univariate function of integers

Diagnostic plots for gMAP analyses

Probability of Success for a 1 Sample Design

Diagnostic plots for EM fits

Operating Characteristics for 2 Sample Design

Ankylosing Spondylitis.

Exact Confidence interval for Binary Proportion

Ulcerative Colitis.

Summarize Arrays

Functional programming utilities

Crohn's disease.

Scrambles the order of a mcmc array object for usage as a mcmc sample. It is advisable to set order once per mcmc run, otherwise correlations in the mcmc sample will be lost.

Automatic Fitting of Mixtures of Conjugate Distributions to a Sample

R Bayesian Evidence Synthesis Tools

Fast column-wise calculation of unbiased variances