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puniform
Meta-Analysis Methods Correcting for Publication Bias
View on CRAN: Click
here
Download and install puniform package within the R console
Install from CRAN:
install.packages("puniform")
Install from Github:
library("remotes")
install_github("cran/puniform") Install by package version:
library("remotes")
install_version("puniform", "0.2.7") Attach the package and use:
library("puniform")
Maintained by
Robbie C.M. van Aert
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.puniform . https://github.com/RobbievanAert/puniform/issues . https://github.com/RobbievanAert/puniform . puniform results . puniform.pdf . puniform_0.2.7.tar.gz . puniform_0.2.7.zip . puniform_0.2.7.zip . puniform_0.2.7.zip . puniform_0.2.7.tgz . puniform_0.2.7.tgz . puniform_0.2.7.tgz . puniform_0.2.7.tgz . puniform archive . https://CRAN.R-project.org/package=puniform .
First Published: 2018-07-05
Latest Update: 2023-09-19
Description:
Provides meta-analysis methods that correct for
publication bias and outcome reporting bias. Four methods and a visual tool
are currently included in the package. The p-uniform method as described in
van Assen, van Aert, and Wicherts (2015)
can be used for estimating the average effect size, testing the null hypothesis
of no effect, and testing for publication bias using only the statistically
significant effect sizes of primary studies. The second method in the package
is the p-uniform* method as described in van Aert and van Assen (2023)
. This method is an extension of the p-uniform
method that allows for estimation of the average effect size and the
between-study variance in a meta-analysis, and uses both the statistically
significant and nonsignificant effect sizes. The third method in the package
is the hybrid method as described in van Aert and van Assen (2018)
. The hybrid method is a meta-analysis method
for combining a conventional study and replication/preregistered study while
taking into account statistical significance of the conventional study. This
method was extended in van Aert (2023) such that it allows for the inclusion
of multiple conventional and replication/preregistered studies. The p-uniform
and hybrid method are based on the statistical theory that the distribution
of p-values is uniform conditional on the population effect size. The fourth
method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as
described in van Aert and van Assen (2018) .
This method computes posterior probabilities for four true effect sizes (no,
small, medium, and large) based on an original study and replication while
taking into account publication bias in the original study. The method can
also be used for computing the required sample size of the replication akin
to power analysis in null-hypothesis significance testing. The meta-plot is
a visual tool for meta-analysis that provides information on the primary
studies in the meta-analysis, the results of the meta-analysis, and
characteristics of the research on the effect under study (van Assen et al., 2023).
Helper functions to apply the Correcting for Outcome Reporting Bias (CORB)
method to correct for outcome reporting bias in a meta-analysis (van Aert &
Wicherts, 2023).
How to cite:
Robbie C.M. van Aert (2018). puniform: Meta-Analysis Methods Correcting for Publication Bias. R package version 0.2.7, https://cran.r-project.org/web/packages/puniform. Accessed 08 May. 2025.
Previous versions and publish date:
Other packages that cited puniform R package
View puniform citation profile
Other R packages that puniform depends,
imports, suggests or enhances
Complete documentation for puniform
Functions, R codes and Examples using
the puniform R package
Some associated functions: data.mccall93 . diffprior . escompute . fe_ma . fis_trans . hybrid . meta_plot . puni_star . puniform . req_ni_r . snapshot . snapshot_naive . var_boot_fis . var_boot_rmd . var_dif_fis . var_dif_rmd . var_pop .
Some associated R codes: LR_test.R . RcppExports.R . approx_puni.R . bisect.R . boot_het.R . bounds.R . bounds_nsig.R . data.R . diffprior.R . draw_plot_g.R . draw_plot_or.R . draw_plot_r.R . escompute.R . esest.R . esest_nsig.R . fe_ma.R . fis_trans.R . get_cv_P.R . get_profile_ci.R . hy.R . hy0.R . hybrid.R . hyr.R . meta_plot.R . ml.R . ml_hy.R . ml_star.R . ml_star_est.R . ml_star_tau.R . optim_ni_r.R . pdist_hy.R . plottrans.R . print.hybridoutput.R . print.puni_staroutput.R . print.puniformoutput.R . pubbias.R . pubbias_nsig.R . puni_star.R . puniform.R . repl.R . report.R . req_ni_r.R . snapshot.R . snapshot_naive.R . testeffect.R . testeffect_nsig.R . testhetero.R . transform_nsig.R . transform_puni.R . var_boot_fis.R . var_boot_rmd.R . var_dif_fis.R . var_dif_rmd.R . var_pop.R . zzz.R . Full puniform package functions and examples
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