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RBesT (version 1.3-1)

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).

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Version

Install

install.packages('RBesT')

Monthly Downloads

1,346

Version

1.3-1

License

GPL (>= 3) | file LICENSE

Maintainer

Sebastian Weber

Last Published

December 21st, 2017

Functions in RBesT (1.3-1)

Ulcerative Colitis.

Crohn's disease.

Summarize Arrays

Automatic Fitting of Mixtures of Conjugate Distributions to a Sample

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

Functional programming utilities

R Bayesian Evidence Synthesis Tools

Ankylosing Spondylitis.

Exact Confidence interval for Binary Proportion

Transform Densities with a link function

Effective Sample Size for a Conjugate Prior

Matrix version of log_sum_exp

Numerically stable summation of logs

Normal Mixture Density

Operating Characteristics for 1 Sample Design

Decision Function for 1 Sample Designs

decision1S_boundary

Decision Boundary for 1 Sample Designs

Combine Mixture Distributions

Difference of mixture distributions

Predictive Distributions for Mixture Distributions

Predictions from gMAP analyses

k nearest neighbor algorithm for multi-variate data

Read and Set Likelihood to the Corresponding Conjugate Prior

Operating Characteristics for 2 Sample Design

Beta-Binomial Probabilities

Probability of Success for 2 Sample Design

Conjugate Posterior Analysis

Diagnostic plots for EM fits

Diagnostic plots for gMAP analyses

Find root of univariate function of integers

Meta-Analytic-Predictive Analysis for Generalized Linear Models

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.

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

Decision Function for 2 Sample Designs

decision2S_boundary

Decision Boundary for 2 Sample Designs

The Gamma Mixture Distribution

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

Plot mixture distributions

Probability of Success for a 1 Sample Design

Fill numeric objects

Beta Mixture Density

Utility function to instantiate 2 parameter mixture densities.

Fit of Mixture Densities to Samples

Extract log likelihood from fitted EM objects

Robustify Mixture Priors

Support of Distributions

Mixture Distributions