Package: metaBMA
Type: Package
Date: 2023-09-12
Title: Bayesian Model Averaging for Random and Fixed Effects
        Meta-Analysis
Version: 0.6.9
Authors@R: c(person(given = "Daniel W.",  
                    family = "Heck", 
                    email="daniel.heck@uni-marburg.de", 
                    role=c("aut","cre"),
                    comment = c(ORCID = "0000-0002-6302-9252")),
             person(given = "Quentin F.",  
                    family = "Gronau", 
                    email="quentingronau@web.de", 
                    role = "ctb"),
             person(given = "Eric-Jan",  
                    family = "Wagenmakers", 
                    email="ej.wagenmakers@gmail.com", 
                    role = "ctb"),
             person(given = "Indrajeet", 
                    family = "Patil", 
                    email = "patilindrajeet.science@gmail.com", 
                    role = "ctb",
                    comment = c(ORCID = "0000-0003-1995-6531")))
Description: Computes the posterior model probabilities for standard
        meta-analysis models (null model vs. alternative model assuming
        either fixed- or random-effects, respectively). These posterior
        probabilities are used to estimate the overall mean effect size
        as the weighted average of the mean effect size estimates of
        the random- and fixed-effect model as proposed by Gronau, Van
        Erp, Heck, Cesario, Jonas, & Wagenmakers (2017,
        <doi:10.1080/23743603.2017.1326760>). The user can define a
        wide range of non-informative or informative priors for the
        mean effect size and the heterogeneity coefficient. Moreover,
        using pre-compiled Stan models, meta-analysis with continuous
        and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors
        can be fitted and tested. This allows to compute Bayes factors
        and perform Bayesian model averaging across random- and
        fixed-effects meta-analysis with and without moderators. For a
        primer on Bayesian model-averaged meta-analysis, see Gronau,
        Heck, Berkhout, Haaf, & Wagenmakers (2021,
        <doi:10.1177/25152459211031256>).
Depends: R (>= 4.0.0), Rcpp (>= 1.0.0), methods
Imports: bridgesampling, coda, LaplacesDemon, logspline, mvtnorm,
        RcppParallel (>= 5.0.1), rstan (>= 2.26.0), rstantools (>=
        2.3.0)
Suggests: testthat, knitr, rmarkdown, spelling
LinkingTo: BH (>= 1.78.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.9.1),
        RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>=
        2.26.0)
VignetteBuilder: knitr
URL: https://github.com/danheck/metaBMA,
        https://danheck.github.io/metaBMA/
License: GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: yes
SystemRequirements: GNU make
Biarch: true
Language: en-US
RoxygenNote: 7.2.3
Packaged: 2026-10-29 11:10:56 UTC; root
Author: Daniel W. Heck [aut, cre]
        (<https://orcid.org/0000-0002-6302-9252>), Quentin F. Gronau
        [ctb], Eric-Jan Wagenmakers [ctb], Indrajeet Patil [ctb]
        (<https://orcid.org/0000-0003-1995-6531>)
Maintainer: Daniel W. Heck <daniel.heck@uni-marburg.de>
Config/pak/sysreqs: make
Repository: https://cran.r-universe.dev
Date/Publication: 2023-09-13 08:31:55 UTC
RemoteUrl: https://github.com/cran/metaBMA
RemoteRef: HEAD
RemoteSha: ca20fc6c3225b0a64d144585a10b609485f703c0