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bayesmeta (version 2.6)

Bayesian Random-Effects Meta-Analysis

Description

A collection of functions allowing to derive the posterior distribution of the two parameters in a random-effects meta-analysis, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) .

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Version

Install

install.packages('bayesmeta')

Monthly Downloads

660

Version

2.6

License

GPL (>= 2)

Maintainer

Christian Roever

Last Published

December 15th, 2020

Functions in bayesmeta (2.6)

GoralczykEtAl2011

Liver transplant example data

SnedecorCochran

Artificial insemination of cows example data

8-schools example data

Pediatric liver transplant example data

Aspirin after myocardial infarction example data

SidikJonkman2007

Postoperative complication odds example data

JIA example data

Ankylosing spondylitis example data

Fly counts example data

RhodesEtAlPrior

Heterogeneity priors for continuous outcomes (standardized mean differences) as proposed by Rhodes et al. (2015).

Expected local-information-ratio ESS

forest.bayesmeta

Generate a forest plot for a bayesmeta object (based on the metafor package's plotting functions).

The Rayleigh distribution.

The Lomax distribution.

TurnerEtAlPrior

(Log-Normal) heterogeneity priors for binary outcomes as proposed by Turner et al. (2015).

bayesmeta-package

Bayesian Random-Effects Meta-Analysis

Inverse-Chi distribution.

Half-normal, half-Student-t and half-Cauchy distributions.

Half-logistic distribution.

Bayesian random-effects meta-analysis

funnel.bayesmeta

Generate a funnel plot for a bayesmeta object.

Unit information standard deviation

forestplot.escalc

Generate a forest plot for an escalc object (based on the forestplot package's plotting functions).

Generate summary plots for a bayesmeta object.

forestplot.bayesmeta

Generate a forest plot for a bayesmeta object (based on the forestplot package's plotting functions).

Compute normal mixtures

Posterior predictive \(p\)-values