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RBesT (version 1.3-7)

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

1.3-7

License

GPL (>= 3) | file LICENSE

Maintainer

Sebastian Weber

Last Published

November 19th, 2018

Functions in RBesT (1.3-7)

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.

Functional programming utilities

R Bayesian Evidence Synthesis Tools

Ulcerative Colitis.

Ankylosing Spondylitis.

Exact Confidence interval for Binary Proportion

decision2S_boundary

Decision Boundary for 2 Sample Designs

Crohn's disease.

Transform Densities with a link function

Effective Sample Size for a Conjugate Prior

Beta Mixture Density

Meta-Analytic-Predictive Analysis for Generalized Linear Models

Combine Mixture Distributions

Extract log likelihood from fitted EM objects

Difference of mixture distributions

Beta-Binomial Probabilities

Conjugate Posterior Analysis

Probability of Success for 2 Sample Design

Utility function to instantiate 2 parameter mixture densities.

Decision Function for 1 Sample Designs

Matrix version of log_sum_exp

Diagnostic plots for EM fits

Diagnostic plots for gMAP analyses

Fill numeric 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.

Plot mixture distributions

Mixture Distributions

Fit of Mixture Densities to Samples

Normal Mixture Density

k nearest neighbor algorithm for multi-variate data

Numerically stable summation of logs

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

The Gamma Mixture Distribution

Probability of Success for a 1 Sample Design

Robustify Mixture Priors

Support of Distributions

Find root of univariate function of integers

Summarize Arrays

Automatic Fitting of Mixtures of Conjugate Distributions to a Sample

decision1S_boundary

Decision Boundary for 1 Sample Designs

Decision Function for 2 Sample Designs

Read and Set Likelihood to the Corresponding Conjugate Prior

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

Operating Characteristics for 1 Sample Design

Operating Characteristics for 2 Sample Design

Predictive Distributions for Mixture Distributions

Predictions from gMAP analyses

Fast column-wise calculation of unbiased variances