Package: puniform
Type: Package
Title: Meta-Analysis Methods Correcting for Publication Bias
Version: 0.2.4
Date: 2020-12-21
Author: Robbie C.M. van Aert
Maintainer: Robbie C.M. van Aert <R.C.M.vanAert@tilburguniversity.edu>
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) <doi:10.1037/met0000025> 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 (2019)
<doi:10.31222/osf.io/zqjr9>. 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 (2017)
<doi:10.3758/s13428-017-0967-6>. The hybrid method is a meta-analysis method
for combining an original study and replication and while taking into account
statistical significance of the  original study. 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) <doi:10.1371/journal.pone.0175302>. 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., 2021). 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, 2021).
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.15), ADGofTest (>= 0.3), stats, metafor
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.1.1
URL: https://github.com/RobbievanAert/puniform
BugReports: https://github.com/RobbievanAert/puniform/issues
NeedsCompilation: yes
Packaged: 2021-01-05 17:25:27 UTC; s787802
Repository: CRAN
Date/Publication: 2021-01-06 01:20:02 UTC