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RBesT (version 1.7-3)

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

1,532

Version

1.7-3

License

GPL (>= 3)

Maintainer

Sebastian Weber

Last Published

January 8th, 2024

Functions in RBesT (1.7-3)

Decision Function for 2 Sample Designs

Meta-Analytic-Predictive Analysis for Generalized Linear Models

decision1S_boundary

Decision Boundary for 1 Sample Designs

Effective Sample Size for a Conjugate Prior

Beta-Binomial Probabilities

Decision Function for 1 Sample Designs

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

Extract log likelihood from fitted EM objects

Transform Densities with a link function

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.

Read and Set Likelihood to the Corresponding Conjugate Prior

Beta Mixture Density

Mixture Distributions

Fit of Mixture Densities to Samples

Numerically stable log of the inv_logit function

k nearest neighbor algorithm for multi-variate data

Utility function to instantiate 2 parameter mixture densities.

Plot mixture distributions

Multivariate Normal Mixture Density

Mixture distributions as brms priors

The Gamma Mixture Distribution

Normal Mixture Density

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

Diagnostic plots for EM fits

Operating Characteristics for 1 Sample Design

Diagnostic plots for gMAP analyses

Combine Mixture Distributions

Find root of univariate function of integers

Predictions from gMAP analyses

Robustify Mixture Priors

Conjugate Posterior Analysis

Predictive Distributions for Mixture Distributions

Difference of mixture distributions

Operating Characteristics for 2 Sample Design

Probability of Success for a 1 Sample Design

Probability of Success for 2 Sample Design

Support of Distributions

Functional programming utilities

R Bayesian Evidence Synthesis Tools

Exact Confidence interval for Binary Proportion

Automatic Fitting of Mixtures of Conjugate Distributions to a Sample

Crohn's disease.

Ankylosing Spondylitis.

Ulcerative Colitis.

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.

Fast column-wise calculation of unbiased variances

Summarize Arrays

decision2S_boundary

Decision Boundary for 2 Sample Designs

Fill numeric objects