Learn R Programming

bayesmeta (version 3.5)

Bayesian Random-Effects Meta-Analysis and Meta-Regression

Description

A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, 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) , or Roever C and Friede T (2022) .

Copy Link

Version

Install

install.packages('bayesmeta')

Monthly Downloads

3,190

Version

3.5

License

GPL (>= 2)

Maintainer

Christian Roever

Last Published

August 29th, 2025

Functions in bayesmeta (3.5)

SchmidliEtAl2017

Historical variance example data
bayesmeta

Bayesian random-effects meta-analysis
SidikJonkman2007

Postoperative complication odds example data
bayesmeta-package

Bayesian Random-Effects Meta-Analysis and Meta-Regression
Rubin1981

8-schools example data
RobergeEtAl2017

Aspirin during pregnancy example data
convolve

Convolution of two probability distributions
bmr

Bayesian random-effects meta-regression
TurnerEtAlPrior

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

Artificial insemination of cows example data
drayleigh

The Rayleigh distribution.
dhalflogistic

Half-logistic distribution.
dlomax

The Lomax distribution.
dhalfnormal

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

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

Inverse-Chi distribution.
forestplot.bmr

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

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

Effective sample size (ESS)
forestplot.escalc

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

Generate a funnel plot for a bayesmeta object.
pppvalue

Posterior predictive \(p\)-values
weightsplot

Illustrate the posterior mean weights for a bayesmeta object.
traceplot

Illustrate conditional means of study-specific estimates as well as overall mean (or other linear combinations) as a function of heterogeneity.
uisd

Unit information standard deviation
kldiv

Kullback-Leibler divergence of two multivariate normal distributions.
plot.bayesmeta

Generate summary plots for a bayesmeta object.
summary.bmr

Summarizing a bmr object).
normalmixture

Compute normal mixtures
GoralczykEtAl2011

Liver transplant example data
NicholasEtAl2019

Multiple sclerosis disability progression example data
Peto1980

Aspirin after myocardial infarction example data
CrinsEtAl2014

Pediatric liver transplant example data
BucherEtAl1997

Direct and indirect comparison example data
KarnerEtAl2014

COPD example data
HinksEtAl2010

JIA example data
BaetenEtAl2013

Ankylosing spondylitis example data
Cochran1954

Fly counts example data
RhodesEtAlPrior

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